Methods and apparatus for monitoring consciousness

ABSTRACT

The systems of the present invention provide improved accuracy in monitoring, analysing, detecting, predicting and/or providing alerts and alarms associated with depth of anaesthesia, depth of consciousness, hypnotic state, sedation depth, fatigue or vigilance of a subject, with as few as 3 surface electrodes. The systems incorporate real-time phase, amplitude and frequency analysis of a subject&#39;s electro-encephalogram. The systems weight outputs of various types of analyses to produce an integrated analysis or display for precise indication or alert to users of the systems including anaesthetists, nurses and other medical personnel, transport drivers and machine workers. The systems weight the outputs of one or more analysis algorithms including combinations of simultaneous, real-time R&amp;K analysis, AEP spectral analysis-SEF-MF, Bi-coherence analysis, initial wave analysis, auditory response, arousal analysis, body movement analysis, 95% spectral edge analysis and anaesthetic phase and spectral energy variance measurement in association with a subject&#39;s state of consciousness.

FIELD OF THE INVENTION

The present invention relates to diverse methods and apparatus includingsystems incorporating same, for selectively monitoring the state ofmind, or state of consciousness of human and other sentient subjects.More particularly the present invention relates to novel sensors andsuites of sensors for accurately monitoring, sensing, tracking,analysing, storing, logging and/or displaying data related tocombinations of physiological senses of a sentient subject. Thephysiological senses may include mind state and arousal of the subjectincluding frequency, phase, amplitude and/or activity of one or moreelectro-encephalogram (EEG) signals.

The apparatus may be used in various configurations for applicationsincluding, inter alia, depth of consciousness, depth of unconsciousness,depth of anaesthesia, state of a subject's alertness, depth of sedation,hypnotic state, state of concentration, state of vigilance and state ofattention. In a particular application, the system of the presentinvention may be adapted to monitor a subject for depth of anaesthesiaand/or present state of consciousness during anaesthesia administrationso that eg. the subject may be properly sedated during a medicalprocedure. In addition, various data collecting and processingtechniques are described pursuant to the systems of the presentinvention, as well as dynamic, re-configurable and adaptable displayconfigurations for such data. An operator may reference such data asmost optimally relates to the application (or applications) set forthherein in readily understandable format including suitable alarmsignalling, threshold monitoring and the like.

The systems may utilize sleep analysis, EEG bispectral analysis(incorporating bi-coherence) and audio evoked potential (AEP) analysisin an integrated fashion for improved monitoring of, inter alia, asubject's consciousness, audio sensory systems, movement, arousal,muscle activity, eye movement, eye opening, stress and anxiety levels,vital sign parameters, and/or audio-visual recall. The monitoringsystems preferably are arranged such that associated physiologicalelectrode attachments are minimized.

The present invention is related to systems disclosed in PCT applicationAU99/01166 filed on 24 Dec. 1999 entitled “Vigilance Monitoring System”,the disclosure of which is incorporated herein by cross reference.

BACKGROUND OF THE INVENTION

William Thomas Gordon Morton first demonstrated what is today referredto as surgical anaesthesia. However, a comprehensive or detailedunderstanding of how anaesthesia works is still unknown today. It isknown that anaesthesia acts upon the central nervous system by reactingwith membranes of nerve cells in the brain in order to shut downresponses such as sight, touch and awareness, but the precise mechanismsand affects of this sensory process are still a subject of research.

In Australia about 1 million people a year undergo general anaesthesia.Of these 1 million people about 5 people die each year, as a directresult of the anaesthesia, while about 3000 more will be inadequatelyanaesthetised. These inadequately anaesthetised people will experience arange of symptoms from hearing recall while undergoing a medicalprocedure, sight recall from premature recovery and the early opening ofeyes, stress and anxiety from experiencing paralysis. Some degree ofmental awareness to the medical procedure being instigated, memoryrecall from having some degree of consciousness, and operation mishapscan occur in cases where the subject's state of paralysis is notadequate leading to movement of the subject's body during incision, forexample.

A typical general anaesthetic procedure may involve a pre-medication orsedative, after which the patient is wheeled into the operating theatrewhere the anaesthetist applies a blood-pressure measurement cuff to thepatient's arm, an oximeter probe to the patient's finger for themeasurement of oxygen saturation, and ECG or electrocardiogram leads toa patient's chest for monitoring of heart-rate.

An intravenous cannula is then inserted into the patient's arm, and amixture of drugs are infused into the blood-stream in order to put thepatient to sleep, control pain and relax muscles. Within about 30seconds the patient will typically transition from a state ofconsciousness to unconsciousness. Once the patient is unconscious, theanaesthetist typically reverts the patient to a gas delivery mask, whichcontains an “inhalation” anaesthetic that is breathed, by the patientthrough the mask. The patient may also be attached to a ventilator thatwill assist or support the patient's ventilation during the operation.The surgeon's intent is to commence the medical operation procedure whenthe patient is unconsciousness and can feel no pain.

The current state of the art provides an array of systems to monitor apatient whilst undergoing anaesthetic drug delivery, but none of theseaccommodate monitoring and validation of the range of sensory parameterssatisfactory to monitor for “shut-down” or unconscious state of neuralrecall (including state of hypnosis; unconsciousness and sleep),auditory recall state (including Audio Evoked Potential and complexfrequency and sensitivity state), muscle paralysis, movement and arousalstate (including arousal and body movement analysis), visual recallstate, (including eye opening and eye movement analysis state), anxietyand stress state (including temperature, blood-pressure, oxygensaturation-SA02, heart-rate variability, skin galvanometry resistanceanalysis).

Some prior art systems provide analysis of unconsciousness state (AspectMonitoring) and other systems analyse electro-encephalograph signalactivity (Physiometrix). Moreover experiments have been conducted andapparatus devised to monitor audio response (Audio Evoked Potential)together with a range of neurological analysis. However, the working ofthe brain's responses to anaesthetics and subsequent “shut-down” of thebody's sensory systems still remains a mystery.

The system of the present invention may measure not only the state ofconsciousness of the sentient subject but also various states of sensorysystems. In particular emphasis may be applied to measurement andmonitoring of the sensory systems that are potentially most vulnerableto incidence of recall during an anaesthetic procedure. The HCM systemof the present invention may provide a primary measure or guide to aclinician for optimal anaesthetic drug dosage by monitoringconsciousness (such as associated with EEG and BSAEP parametermeasurement), while also providing a “last line of defence” bymonitoring the subject's sensory systems including sight, hearing,movement, taste and sound, for minimizing risk of recall associated withan anaesthetic/medical procedure.

Allan Rechtschaffen and Anthony Kales, describe in “A Manual ofStandardized Terminology, Techniques and Scoring System for Sleep Stagesof Human Subjects”, Brain Information Service/Brain Research Institute,University of California Los Angeles, Calif. 90027, (R&K) (34) a methodof scoring human sleep physiology. Further descriptions of the behaviourof the brain's electrical energy in terms of half-period amplitudeanalysis are disclosed by Burton and Johns in AU Patent 632932, thedisclosure of which is incorporated herein by cross reference (45).

These earlier techniques were utilised for defining stages of a human'ssleep and were predominantly applied to a subject in sleep, asrecognised by conventional stages of sleep including stage 1, stage 2,stage 3, stage 4 and REM sleep (as distinct from hypnotic or in-depth ofanaesthesia states). In particular the first stage of sleep detectionwith R&K standardised sleep staging techniques relies upon specificphysiological sequences of events, such as the subject's rolling of theeyes or slow moving electro occulogram and changes in theelectro-encephalogram frequency spectrum. It is apparent thatsignificant changes in human physiology leading to the subject enteringstage one of sleep represent a dramatic change in a subject's state ofconsciousness. This dramatic state of consciousness may be too late indetection where the aim is, for example, to determine onset of a lack ofvigilance for a pilot of an aircraft or other critical job function. Inother circumstances a subject could enter a hypnotic state where thedriver of a car, for example, lapses into a type of “trance” and thestate of vigilance and the subject's environment could become criticaland highly dangerous. The phases of human physiology periods (leading upto stage 1) of non-sleep are not specifically described in R&Kteachings.

Even hospitals such as Melbourne's Alfred Hospital, which demonstratedone of the world's lowest reported incidences of consciousness undergeneral anaesthesia, still have an incidence rate of 1 in 1000 patients(91). The chances of being aware and experiencing pain are even lowerbut the consequences can be devastating. Side effects of consciousnesswhile under anaesthesia can range from nightmares to recall of pain,stress, visual and audio recall during a medical procedure.

The HCM system of the present invention may address these limitations byproviding specialised R&K and bicoherence monitoring during applicationof general anaesthesia. The HCM system may also provide methods ofartefact rejection to allow more precise monitoring and analysis ofneurological and other bicoherence and sleep variables from the subject.

Until now there has been no way to determine whether a patient is asleepduring a medical procedure, according to University ofSydney-Australia's Web site, introductory paper on anaesthesia (92).

In 1942 Canadian anaesthetists discovered that neuromuscular blockingdrugs could be developed. Sir Walter Raleigh had known in 1596 that theindigenous people of Bolivia had been using an American plant derivativecalled curare to cause paralysis. Since 1942 these drugs haverevolutionised surgery, particularly abdominal and chest operationswhere muscle contraction had made cutting and stitching almostimpossible.

By deactivating the muscles, anaesthetists can make lighter and saferanaesthetic drugs whilst still keeping the patient unconscious. Thesemuscle blocking drugs are now used in up to half of all operations.However, the downside of the application of these muscle drugs is that apatient is paralysed so that conscious or unconscious movement isimpossible. In circumstances where a patient is awakening or is in astate of consciousness during a medical procedure, the patient is unableto move and defend him/herself or alert anyone of a potentially horrificexperience that the patient may be encountering.

Anaesthetists tend to overestimate the amount of anaesthetic drug usageby up to 30%. This overestimation has consequences in relation to apatient's health, recovery time and financial costs to health services(94).

The HCM system of the present invention may address the limitations ofthe prior art by providing an apparatus and method for monitoring andanalysing arousal and body movement of a patient throughout anaesthesia.Furthermore the HCM system may provide means to position electrodes andsensors for monitoring arousal and body movements from any location onthe patient's body. If, for example, a chest operation requires extremeabsence from movement due to a critical incision procedure, electrodesor sensors may be placed around sensitive chest muscles non-invasivelyor via inter-operative methods.

The challenge to monitor for appropriate or optimum anaesthesia isdemonstrated with classic experiments such as that of psychiatristBernard Levin in 1965, when 10 patients who were read statements duringanaesthesia, later had no recall of the statements when questioned aftersurgery. However, of the same patients under hypnosis four could quotethe words verbatim and another four could remember segments, but becameagitated and upset during questioning (95). An adequately anaesthetisedpatient should not “feel”, “smell”, “see” or “taste” anything until theyregain consciousness (96).

In 1998 Dr David Adams of New York's Mount Sinai Medical Centre replayedaudio tapes of paired words (boy/girl, bitter/sweet, ocean/water . . . )to 25 unconscious heart surgery patients. Approximately four days afterthe operation, the patients listened to a list of single words. Some ofthese words had been played while they were unconscious during theirformer operation. The patients were asked to respond to each word withthe first word that came into their minds. The patients were found to besignificantly better at free-associating the word pairs they had alreadyencountered than those they had not. It was apparent that the patientshad heard the information and remembered it (97).

It appears that while a smaller number of patient's have consciousmemories of their experiences on the operating table, a larger numberhave unconscious recollections. While positive messages during surgerymay have desired consequences others can have undesirable results (98).

The HCM system of the present invention addresses the limitations of theprior art by providing in one form an apparatus and method formonitoring auditory sensory system while the patient is undergoinganaesthesia. Furthermore the HCM system may provide a comprehensivemeans of analysing both frequency response and sensitivity response ofone or both auditory sensory systems of the patient during anaesthesia.This may provide monitoring and a means of replay as evidence of thestate of the subjects auditory system throughout anaesthesia to reducethe risk of auditory recall.

The HCM system of the present invention may provide a method andapparatus for monitoring and/or analysing a patient's eye movement andeye opening to minimise or eliminate the risk of visual recall afteranaesthesia.

The HCM system may provide a method and apparatus for monitoring apatient's stress and anxiety levels together with a range of vitalparameters to minimize the risk that the patient is undergoing unduestress, anxiety and health conditions during anaesthesia, andsubsequently reducing or eliminating the incidence of these states.

Previous studies present a relationship between human treatment andchanges in physiological states, as associated with anxiety or stress.In particular such studies link respiration rate, skin resistance andfinger pulse volume to anxiety (53). Other studies present relationshipsbetween salivary cortisol levels and activities accompanying increasedcardiovascular activity (54).

Studies also present relationships between heart rate variability (HRV),and people reporting anxiety and perceived stress and between asubject's blood pressure and heart rate, and activities associated withincreased stress (55, 56, 57). Vagal modulation of heart-rate period wasfound to be sensitive to a person's emotional stress. Other studiespresent relationships between a subject's blood pressure and heart rate,and activities associated with increased stress (58).

The HCM system of the present invention may measure, analyse and displayin near real-time graphical or numerical representation of skinresistance, oxygen saturation, pulse-transit-time arousal, bloodpressure, heart rate, heart rate variability and temperature.Furthermore, the HCM system may measure, monitor and analyse thesevariables and present an index and/or other graphical and tabulardisplay means, to assist an anaesthetist or other medical personnel inthe assessment of a subject's depth of anaesthesia.

The HCM system of the present invention may record, monitor and analysein near real-time effects of cortisol salivary content and changesthereof as an indicator of stress or anxiety, as may be associated withincreased heart rate as may occur with premature awaking duringanaesthesia.

The HCM system of the present invention may also measure, analyse anddisplay in near real-time graphical or numerical representation of vagalmodulation of heart-rate period. Furthermore, the HCM system maymeasure, monitor and analyse this variable which may be represented interms of HRV frequency de-composed into various frequency components;ie. LF-0.05-0.15 Hz, HF-0.15-0.5 Hz, using spectral analysis; and maypresent an index and/or other graphical and tabular display means, toassist an anaesthetist or other medical personnel in assessing asubject's depth of anaesthesia.

The HCM system of the present invention may record, monitor and analysein near real-time effects of blood pressure and heart rate, and changesthereof as an indicator of stress or anxiety as may be associated withchanges in blood pressure and heart rate, as may occur with prematureawaking during anaesthesia.

The current field of sleep medicine is not precise in scoring orquantifying human sleep physiology. The degree of “inter-scorer”agreement in determining sleep classification of human physiology is ofthe order of 80 to 90%. Monitoring and analysing the state of a patientduring anaesthesia treatment, and subsequent accurate determination ofthe patient's state depth of anaesthesia at any point in time isimportant to ensure efficacy of the patient's anaesthetic treatment. Tothis end, accurately defining the mechanisms, sequence or sensitivity ofthe sentient mind “shutting down” or re-awakening as associated withvigilance or response to administration of anaesthetics including themind's recall of such events is important for ensuring optimaladministration of anaesthetic agents. The science and knowledgeassociated with sleep staging or scoring of human sleep is stillrelatively primitive in terms of understanding the mechanisms of sleepand consciousness. In particular it appears that the science andknowledge associated with details and the sequence of “shutting down” ofconsciousness and human sensory systems including sight, hearing, smell,consciousness and muscle activity or arousal necessary to avoidpotential recall of a patient's experiences associated with anaesthesia,is still relatively young and inexperienced.

The HCM system of the present invention recognizes the prior artlimitations, and addresses them by providing a system which may beconfigured to monitor and analyse combinations of a subject's sensorysystems during, inter alia, an anaesthesia procedure.

The HCM system may improve the probability of determining a subject'sconsciousness by applying two or more independent methods of analysisincluding bi-coherence based analysis and Brain Stem Audio EvokedPotential or Steady State Evoked Potential based analysis, andarbitrating, cross-checking and integrating results of the two or moremethods of analysis using a further independent method of EEG analysissuch as spectral based EEG analysis, including optimised bi-spectralanalysis and optimised R&K sleep-wake analysis, to improve accuracy indetermining the consciousness status of the subject. In conjunction withdetermining the consciousness status of the subject, the system mayanalyse consciousness/hypnosis/vigilance with the aid monitoring andanalysis of brain waves together with various combinations of sensorymonitoring and analysis including auditory, muscle movement and/orarousal including micro-arousal, eye opening & eye movement.

Other parameters, which may optionally be included indepth-of-anaesthesia monitoring and analysis determination, includeanxiety & stress levels, heart rate variability, galvanomic skinresistance, temperature, respiration rate variability and blood pressureand/or oxygen saturation.

The HCM system of the present invention may include an apparatus formonitoring, analysing, recording and replaying a subject's consciousnessstate in conjunction with critical physiological sensory status of thesubject. In this context critical refers to sensory systems that arecritical for minimising the risk of recalling the experience or senses,associated with a medical procedure while under anaesthesia.

The combinations of multiple sensory monitoring and analysis may includea provision for a user to configure, select or operate the system withone or more channels of input data from a subject together with a rangeof system set-ups or montages, consistent with the complexity of signalattachment to the subject, the critical nature of the monitoringincluding the duration of an operation and risk associated withadministration of anaesthesia or muscle paralysis medication to thesubject, the skill and training or experience of the user, thesensitivity of the subject to anaesthetic or muscle paralysismedication, and variability of different subjects in relation tosusceptibility to premature awakening or consciousness including recallof auditory or visual stimuli, anxiety or arousal.

The HCM system of the present invention may provide unique wirelessconnected electrode systems to reduce conventional wiring and risk ofentanglement

In some instances patient or subject specific data may substantiallyaffect monitoring or analysis methods associated with the monitoringsystem. To the applicants knowledge, no one has linked criticalparameters such as weight, age and sex of a patient to sensitivity andweighting of depth of anaesthesia monitoring. The HCM system of presentinvention may include a capability to adapt weighting or sensitivity ofthe analysis to the physiological parameters being monitored. An exampleof this may include the manner in which the weight or sex of a subjectaffects the optimal band of concentration of an anaesthetic agent.

The HCM system of the present invention may utilise data associated withthe subject, such that its sensitivity or important thresholds may beadjusted from one subject to the next. In this context “utilisation” ofdata refers to compensation of critical display threshold levels andsensitivity of various user displays. In other-words the user displayedthresholds and associated variations in sensitivity may be changed inaccordance with critical (for example, in depth of anaesthesiamonitoring) sensitivity to certain anaesthetic agents.

Surface electrode connections have been applied in the past tomonitoring applications associated with various physiologicalparameters. However one problem with surface electrode connections isthat the quality of the connection to the subject can deteriorate due toa number of conditions including patient sweat, movement or drying outof the connecting electrolyte solution between electrode and subject.The problem of electrode quality may be more critical in applicationssuch as those associated with intensive care and operating theatreenvironments, than is the case with depth of anaesthesia monitoringsystems.

To date, no one has used connection of redundant electrodes, automaticvalidation of electrode connection quality and validation by way ofroutine impedance measurements and other signal validation techniques(refer FIG. 18—MFD Block 7) including automatic substitution of poorelectrode connections with redundant or spare electrode connections(refer FIG. 35—IAMES or FIG. 37—ISES). The system of the presentinvention may include redundant electrodes together with integratedelectrode-sensors and wireless/rechargeable electrode-sensors tominimize the number of electrodes and sensors (as few as 3sensor-electrodes in some embodiments) for depth of anaesthesiamonitoring and analysis (where the quantity, reliability and simplicityof electrode-sensor attachments may be highly critical) includingmonitoring and analysis of physiological states such as mind-state,auditory sensory, visual sensory, arousal sensory, anxiety sensory andvital states.

Eye movement sensors (such as piezo or PVD movement sensors) andelectrodes (such as EOG) have been used in the past for detecting eyemovement or eye-lid movement respectively. However one problemassociated with depth of anaesthesia monitoring is that some patientsawaken prematurely during a medical procedure and opening of the eyescan lead to distressing views and subsequent recall or nightmareoccurrences. A further problem exists where the patient may litigate insuch instances, in which case an objective and accurate recording of thepatient's state and amount of eye opening may be important. A systemthat allows the user to calibrate such an eye-opening sensor would alsobe of value. The HCM system of the present invention may provide such asensor (refer FIG. 34—EOS) for detecting in a calibrated manner a degreeof eye opening of a subject.

In accordance with general literature a predominant prior art method fordetecting anaesthesia is bi-coherence analysis of EEG waveforms. AspectMonitoring, which is a main supplier of in-depth anaesthesia monitoringsystems deploys this technique. Aspect Monitoring has trademarkapplications for BIS and Bi-spectral Index. Bi-spectral Index is basedon the technique of bi-coherence analysis.

Functioning of the brain in the transition of states from consciousnessto subconsciousness and from unconsciousness to consciousness isrecognised as a non-linear transition in relation to the generation ofelectrical brain activity. Accordingly, the bi-coherence method ofmonitoring EEG has been shown to be an affective method for predictingthe state of consciousness and the subsequent state of depth ofanaesthesia.

However, even with improved analysis of EEG data as described above,another prior art limitation exists. This limitation is related to thefact that while the combined frequency and phase analysis of EEG datamay provide an improved method for monitoring a patient's state ofconsciousness, it has been found (4) that Audio Evoked Potential (AEP)provides a more informative measure of a subject's transition fromunconsciousness to consciousness, while EEG based bi-spectrum analysisprovides a more informative measure from consciousness tounconsciousness. Accordingly, the HCM system of the present inventionmay automatically detect whether the patient is transitioning fromconsciousness to unconsciousness or visa versa and may apply or weightbispectrum analysis (bicoherence/bispectrum/triple product) or AEPanalysis (such as Brain Stem Auditory Evoked Potential—BAEP)respectively.

The HCM System addresses the limitations of the prior art by applyingR&K analysis as a type of “independent arbitration” agent fordetermining which analysis type is optimal, based on the context andsequence of analysis change or transitions. For example, R&K detectionof wake state, suggests a probable transition from consciousness tounconsciousness, which in turn suggests that the optimal or higherweighting of consciousness state determination should be derived fromBIC (bi-spectral analysis incorporating bi-coherence) analysis. Incontrast, R&K detection of a sleep state (stage 1, 2, 3, 4, REM, forexample) suggests a probable transition from unconsciousness toconsciousness, which in turn suggests that optimal or higher weightingof consciousness state determination should be derived from AEPanalysis.

Barr and colleagues describe in British Journal of Anaesthesia June 2000(1), a Coherence index (CHI) used to assess depth of anaesthesia duringfentanyl and midazolam anaesthesia for coronary bypass surgery in whichBIP decreased during anaesthesia, but varied considerably duringsurgery.

Schraag and colleagues describe in Anesth Analg April 2000 (2), “thatboth BIP and AEPi are reliable means for monitoring the level ofunconsciousness during propofol infusion. However, AEPi proved to offermore discriminatory power in the individual patient. The implication isthat both the coherence index of the electroencephalogram and theauditory evoked potentials index are good predictors of the level ofsedation and unconsciousness during propofol infusion. However, theauditory evoked potentials index offers better discriminatory power indescribing the transition from the conscious to the unconscious state inthe individual patient.”

Gajraj R J describes in British Journal of Anaesthesia May 1999 (3),“Comparison of bi-spectral EEG analysis and auditory evoked potentialsfor monitoring depth of anaesthesia during propofol anaesthesia.” Inthis study, Gajraj & colleagues compared the auditory evoked potentialindex (AEPindex) and bi-spectral index (BIS) for monitoring depth ofanaesthesia in spontaneously breathing surgical patients.” “The averageawake values of AEP-Index were significantly higher than all averagevalues during unconsciousness but this was not the case for BIS. BISincreased gradually during emergence from anaesthesia and may thereforebe able to predict recovery of consciousness at the end of anaesthesia.AEP-Index was more able to detect the transition from unconsciousness toconsciousness.”

Gajraj R J, describes in Br J Anaesth January 1998 (30), “Analysis ofthe EEG bispectrum, auditory evoked potentials and the EEG powerspectrum during repeated transitions from consciousness tounconsciousness.” In this study, Gajraj & colleagues describe: “We havecompared the auditory evoked potential (AEP) index (a numerical indexderived from the AEP), 95% spectral edge frequency (SEF), medianfrequency (MF) and the bi-spectral index (BIS) during alternatingperiods of consciousness and unconsciousness produced bytarget-controlled infusions of propofol.” “Our findings suggest that ofthe four electrophysiological variables, AEP index was best atdistinguishing the transition from unconsciousness to consciousness andtherefore may be able to predict the transition unconsciousness toconsciousness.”

The HCM system of the present invention may address the limitation ofprior art methods of EEG sleep analysis, by applying multipleindependent methods of analysis and processing including methods basedon auditory evoked potential (AEP) index (a numerical index derived fromthe AEP), 95% spectral edge frequency (SEF), median frequency (MF) andcoherence index (CHI) and R&K sleep staging, together with a uniquemethod of context analysis to provide improved decision making withrespect to which of the multiple analysis processes are most suitablefor optimal tracking of each phase of the monitored stages ofconsciousness.

Witte H, describes in: Neurosci Lett November 1997 (5), “Analysis of theinterrelations between a low-frequency and a high-frequency signalcomponent in human neonatal EEG during quiet sleep.” In this study,Witte and colleagues describe: “It can be shown that dominant rhythmicsignal components of neonatal EEG burst patterns (discontinuous EEG inquiet sleep) are characterised by a quadratic phase coupling (coherenceanalysis). A so-called ‘initial wave’ (narrow band rhythm within afrequency range of 3-12 Hz) can be demonstrated within the first part ofthe burst pattern. The detection of this signal component and of thephase coupling is more successful in the frontal region. By means ofamplitude demodulation of the ‘initial wave’ and a subsequent coherenceanalysis the phase coupling can be attributed to an amplitudemodulation, i.e. the envelope curve of the ‘initial wave’ shows for adistinct period of time the same qualitative course as the signal traceof a ‘lower’ frequency component (0.75-3 Hz).”

The HCM system of the present invention may address the limitation ofcategorisation of neonatal neurological patterns by including within thedecisions of sleep-wake categorisation information such as the age of asubject. In turn this information may be used to weight analysisprocesses within the neurological data. In the above case the age of thesubject may prompt the analysis processes to recognise unique markerssuch as ‘initial wave’ and to use recognition of these unique markers toprovide improved accuracy for categorising and detecting EEG patternsand associated sleep staging of neonatal human subjects.

It is apparent that no one singular method for determining a subject'sstate of vigilance is appropriate. R&K standardised criteria for sleepstaging can be important in recognizing a subject's sleep state,coherence analysis can accurately describe a patient's transition fromwake to sleep, auditory response can describe a subject's transitionfrom sleep to wake, “initial wave” can assist in detecting a subject'stransition into hypnotic state, and movement detection can describe asubjects state of rest or relaxation. Furthermore, accuracy in detectingand tracking a subject's vigilance state can be improved by recognizinga subject's age and in appropriate cases utilizing a subject'spersonalised calibration and learning functions. While conventionalmethods of vigilance analysis as described above, each have specificbenefits associated with various forms of sleep state, hypnotic orvigilant state, the HCM system of the present invention is designed toincorporate concurrent or selective combinations of analysis inaccordance with the users specific requirements.

The HCM system of the present invention recognises that the linearamplitude and spectral analysis methods utilised by R&K for sleep stateanalysis of a subject are indifferent to the non-linear coherenceanalysis method more suited for entry and exit from sleep or hypnoticstates of the subject.

The HCM system of the present invention may utilise any combination ofspectral edge frequency analysis, Coherence analysis, R&K standardisedsleep staging criteria, auditory response monitoring, initial wavemonitoring, arousal analysis and specialised input parameters derivedfrom the calibration or specific subject configuration and systemconfigurations such as the subject's sex and age data. A learningfunction and application of neural networks may provide a means for thesystem to weight the vigilance analysis format in a manner which is mostappropriate for a specific subjects vigilant state such as wake, sleep,and transition from wake to sleep or sleep to wake.

The HCM system of the present invention may analyse a subject'sneurological data for purpose of coherence analysis and R&K spectralanalysis that may also include electro-occulogram and electro-myogramphysiological data. In particular the HCM system may process transitionstages of the subject's vigilance to determine the most appropriatemethod of analysis and display of the subject's hypnotic, sleep orvigilance state.

For example, the subject may be detected as being in wake state by meansof R&K analysis (preferred method for sleep/wake detection), followed byon-set of hypnotic state (preferred method of monitoring and analysingexit of hypnotic/sleep state) as detected by the coherence index, entersleep state by means of R&K analysis stage 1 detection (preferred methodfor sleep/wake detection), exit sleep state by means of firstly R&K wakestate detection, and then tracking depth of hypnotic state by means ofAEP index and auditory response (preferred method of monitoring andanalysing exit of hypnotic/sleep state).

The HCM system of the present invention may automatically allocate anoptimal processing means for determining a subjects transition ofconsciousness state or sleep state by applying simultaneously one ormore processing techniques for determining the most appropriate measureof the subjects state in accordance with the transition of the subjectsconsciousness.

Furthermore the HCM system may include frequency analysis (R&K analysis)(34) spectral analysis-SEF-MF, ½ period analysis (46), (FFT) as a meansto determine the transition and the current state of a subject in orderto determine which method of consciousness analysis (BIP, AEP forexample) is the most accurate and subsequent indicator for identifyingand tracking the subject's vigilant state.

An ideal embodiment of the present invention may provide an independentmeasure of both sleep state and brain activity in both wake and sleepstates. Furthermore the ideal embodiment may detect when a non-validsleep state was recognised (per International standard R&K) so thatbrain activity or consciousness measures should be utilised (BIP and AEPindex). Furthermore the ideal embodiment may include a simplenon-ambiguous readout for users of the system.

The HCM system of the present invention includes improved analysis ofdepth of anaesthesia/consciousness/patient state with optimisedsleep-wake R&K analysis, optimised bi-spectral analysis and optimisedAEP analysis. Phase based analysis may be combined with frequencyband-amplitude analysis (spectral analysis) to provide an improvement onphase only or frequency based analysis (refer FIGS. 16, 17, 18, 34, 35,37, 41, 42, 45).

To the applicants knowledge no one has used combinations of Sleep-wake ½period analysis or other forms of R&K or modified R&K analysis, uniqueartefact processing (refer FIG. 18—MFD block 21) combined with speciallyweighted (in accordance with empirical clinical data) and optimisedbi-coherence, triple product and bi-spectral index (refer FIG. 18—MFDBlock 10), and AEP analysis to improve the accuracy in determining thestate of a subject's consciousness.

The HCM system may, within a single monitoring device and singleelectrode device, simultaneously provide a combination of analysis types(and displays thereof) including BIS analysis, AEP index analysis,estimated R&K analysis, arousal analysis, eye movement analysis and eyeopening analysis.

A common problem with frequency-based analysis methods (be it sleep-wakeor bicoherence/bispectrum/triple product) in analysing neurologicaldata, is that the results of the aforementioned types of analyses canchange significantly with seemingly stable physiological conditions. Forexample, substantial increases in EEG activity in the 12 to 18 Hz(theta) frequency band may be observed with administration ofanaesthetic agents in the low to medium concentrations, but high dosesof the same agents may lead to sudden reduced activity in the 12-18 Hzfrequency band and increased activity in the 0.5-3.5 Hz band, followedby burst suppression at extremely high concentrations. Similarly,bicoherence/bispectrum/triple product analysis relies upon “relativelynew principles” for determining the subject's state of consciousness. Incontrast, a well documented and validated method for sleep staging suchas presented by R&K, utilises analysis techniques which, although beinghighly validated, are subject to misleading frequency effects, asdescribed above. Apparatus based on the R&K method combines real-timeoptimised (34, 45) R&K analysis with optimised bi-spectral analysis toincrease accuracy beyond conventional Bi-spectral Index™ (52).Application of optimised spectral analysis may provide a meaningfulbasis for determining consciousness state, where R&K analysis has beenformulated to provide sleep stage (or depth of sleep) or wake state(referred to herein as sleep-wake analysis) as opposed to varyingdegrees of subconsciousness, as a subject approaches sleep or anunconscious state. R&K analysis on the other hand may provide a wellvalidated method for determining a subject's depth of sleep. Furthermoremodified R&K analysis (refer FIG. 18—MFD Block 10) may improve artefactrejection, making determination of the patient state more reliable orless dependent on artefacts or noise, often evident during monitoring ofa patient. The artefacts may include sweat artefact, amplifier blockingartefact, and mains noise signal intrusion, for example. The HCM systemof the present invention may weight optimised R&K and optimisedbi-spectral analysis in accordance with the strengths and weaknesses ofeach of these processes to provide overall improved accuracy andprobability of determining the subject's depth or state of anaesthesia.

The HCM system of the present invention may reduce the effects of overreliance on frequency based changes of neurological data from a patient,by utilising both frequency based EEG (sleep-wake analysis) and phasebased EEG analysis (bicoherence/bispectrum/triple product).

The HCM system may provide automatic selection or weighting of BIC andAEP analysis by means of R&K or similar frequency based analysis as anarbitration agent in the decision path for weighting analysis types.

The HCM System may be adapted to automatically detect whether thepatient is transitioning from consciousness to unconsciousness or visaversa and to apply or weight bi-spectrum analysis(bi-coherence/bi-spectrum/triple) or audio evoked potential analysis(such as Brain Stem Auditory Evoked Potential-BAEP) respectively.

The system of the present invention may monitor and detect the state ofthe subjects consciousness. In particular real-time and concurrentprocesses ideally suited to both non-linear and linear analysistechniques may be applied. The system may include bi-coherence(non-linear) analysis for depth of consciousness monitoring inconjunction with Audio Evoked Potential (more linear based) analysis formonitoring transition of a subject between conscious and unconsciousstates. The system may provide improved monitoring and analysis forapplication in detection, system alerts and alarms associated with depthof anaesthesia, hypnotic state, sedation depth, fatigue or vigilance ofa subject, with as few as 3 surface electrodes. Combined or separateindexes or display methods may provide accurate tracking of thesubject's state of consciousness and transition of conscious state. Thesystem of the present invention may assign patient states of sleep,wake, depth of consciousness, depth of anaesthesia and vigilance inaccordance with analysis states derived from a combination of analysistypes, including in particular BIC and AEP based analysis. Prior artsystems (such as Aspect Monitoring) are limited as they are not asprecise or responsive as an AEP, arousal or EEG activity based systemfor detecting transition and AEP responsiveness to transition but not asgradual a measure (as BIC) for predicting consciousness state.

However a limitation of this prior art method is that the gradual changeof the bicoherence measure may, by nature of the type of the non-linearanalysis prevent a clear or significant emphasis of the subject'stransition state. The transition state is when the subject changes fromconsciousness to unconsciousness or visa versa. This is a critical statewhen monitoring a subjects depth of anaesthesia as a subject who is onthe verge of waking up may need urgent administration of anaesthesia inorder to avoid a serious incident such as the subject awakening during asurgical operation.

For example, a time based curve or graph of the bi-coherence processedsignal can produce a relatively gradual and consistent change whencompared to other validated methods of consciousness monitoring, such asAudio Evoked Potential (AEP) monitoring techniques.

In the case of AEP monitoring, a subject wears a headphone attachmentand is presented with audio stimulus clicks, while at the same time theauditory nerve is monitored. By monitoring the amplitude of the responseof the monitored (via non-invasive surface electrodes attached to asubject's near ear) auditory nerve signal and averaging this signal bysumming a sequence of overlaid traces of this auditory signal, it ispossible to measure a degree of the subject's consciousness. In thisparticular example consciousness may be determined by a measure of thesubjects hearing responses. One advantage of this method is that it isrecognised to provide superior transition state information, where thetransition state is the actual determinant of whether the subject is ina state of consciousness or unconsciousness. A disadvantage of thismethod is that the state of transition based on AEP analysis isrelatively sudden due to the sudden response of the auditory nerveduring the transition of a subject's state from unconsciouness toconsciouness (30). However, an advantage is the explicit or obviousnature of the data curve transition between the two states.

Therefore the recognised methods of tracking consciousness andunconsciousness of a subject each have different advantages anddisadvantages (33).

However the applicant is not aware of any prior art system or methodthat is able to provide an ideal solution. Such solution would need tohave non-linear gradual measurement and prediction abilities associatedwith bi-coherence analysis, together with immediate indicationassociated with the transition state as depicted by AEP analysis.

The HCM system of the present invention may automatically detect whetherthe patient is transitioning from consciousness to unconsciousness orvisa versa and apply or weight bi-spectrum analysis(bi-coherence/bi-spectrum/triple product) or audio evoked potentialanalysis (such as Brain Stem Auditory Evoked Potential—BSAEP)respectively. The HCM system may address prior art imitations byapplying R&K analysis as a type of “independent arbitration” agent fordetermining which analysis type is optimal, based on the context andsequence of analysis change or transitions. For example, R&K detectionof wake state, suggests a probable transition from consciousness tounconsciousness, which in turn suggests that optimal or higher weightingof consciousness state determination should be derived from the BIC(bi-spectral analysis incorporating bi-coherence) analysis. In contrast,R&K detection of a sleep state (stage 1, 2, 3, 4, REM, for example)suggests a probable transition from unconsciousness to consciousness,which in turn suggests that optimal or higher weighting of consciousnessstate determination should be derived from AEP analysis.

An ideal system for monitoring depth of anaesthesia or vigilance ordepth of sedation or hypnotic state should be able to present a singleor simple index, display reference or monitoring technique which clearlydepicts both a prediction of depth of anaesthesia and a current stateand transition of states of a subject. In particular the ideal systemshould be able to utilise a method of combining AEP and bi-coherenceanalysis techniques into a single monitoring measure. The HCM system ofthe present invention may achieve this scenario by weighting the AEPtransition state and the bi-coherence analysis value so that a singlecombined reference is obtained.

The HCM system may weight the transition state heavily when a subjecttransitions his/her mind-state from unconsciousness to consciousness(AEP, arousal and eye opening wake analysis is heavily weighed) so thatan anaesthetist can have a guide in predicting the depth of anaesthesiautilising the bi-coherence factor, but if the subject changes orapproaches a change in state as indicated via AEP analysis, theanaesthetist may be given immediate and obvious display indication andcan avert a potentially serious incident such as the subject awakeningduring a surgical operation.

The HCM system of the present invention may assign patient states ofsleep, wake, depth of consciousness, depth of anaesthesia and vigilancein accordance with analysis states derived from a combination ofanalysis types including R&K analysis (34), AEP (30), spectralanalysis-SEF-MF (4), Bi-coherence (BIC) analysis (33), initial waveanalysis (5), auditory response (4, 30), arousal analysis (35) and bodymovement analysis (34, 26), 95% spectral edge analysis (36) andanaesthetic phase and spectral energy variance measurement inassociation with a subject's state of consciousness (30), PulseTransient Time (PTT) based arousal detection (31), PTT measure and PTTbased blood-pressure reference measure, PTT based heart rate and bloodpressure with simple non-invasive oximeter (31, 32), PAT analysis forsympathetic arousal detection (104-108), EEGspike-Kcomplex-wave-activity-event categorisation (47) andbio-blanket-heart-temperature-PTT blood-pressure-respiration-breathingsound (49).

The HCM system of the present invention may include automaticconsciousness state context determination (refer FIGS. 16, 17, 18, 34,35, 37, 41, 42, 45). The HCM system may provide trend or sequenceanalysis with improved qualification of a subject's depth or level ofvarious mind states by incorporating preliminary analysis or previewanalysis context determination. In particular the HCM system may applyconcurrently and in real-time EEG frequency (26, 30, 36, 47), EEG phase(33) and EEG amplitude analysis (30).

For the purpose of “context” determination, the HCM system may applyconcurrently and in real-time a combination of methods of analysisincluding R&K analysis (34, 45, 46), AEP (30), spectral analysis-SEF-MF(4, 30), Bi-coherence (BIC) analysis (33), initial wave analysis (5),Auditory Evoked Response (30), arousal analysis (35) and body movementanalysis (34), 95% spectral edge analysis (36) and anaesthetic phase andspectral energy variance measurement in association with a subject'sstate of consciousness. (36), Pulse Transient Time (PTT) based arousaldetection (31, 32), PTT measure and PTT based blood-pressure referencemeasure, PTT based heart rate and blood pressure with simplenon-invasive oximeter, PAT analysis for sympathetic arousal detection(104-108), EEG spike-K-complex-wave-activity-event categorisation (47)and bio-blanket-heart-temperature-PTTblood-pressure-respiration-breathing sound (49), to determine thecontext of a subject's state of mind. In particular the “context’ mayinclude that a subject is in a state of wake or consciousness andwhether or not the subject is entering or approaching a state ofunconsciousness or sleep, for example. Where a subject is in a state ofunconsciousness or sleep, an ideal depth and state of consciousnessmonitoring system may emphasise or highly weight a change of state where(for example), this change of state could represent a subject awakeningduring an operation procedure, for example.

There are a number of limitations associated with current standards forstaging human sleep (R&K standardised sleep criteria) (34). Some ofthese limitations arise, for example, from the fact that it has beenfound that infants exhibit higher amplitude of EEG frequency bands suchas deltawave than do more elderly patients. It has also been found thatin infants conventional methods of scoring sleep are not an accurateindication of the child's sleep physiology.

The HCM system of the present invention may address the limitation ofprior art methods of EEG sleep analysis with an ability to concurrentlyanalyze and process a selection of, or combination of methods ofsleep/hypnosis/arousal/vital signs monitoring including:

-   -   R&K analysis (34),    -   EEG pattern recognition    -   AEP (30),    -   spectral analysis-SEF-MF (4),    -   Bi-coherence (BIC) analysis (33),    -   initial wave analysis (5),    -   auditory response (30),    -   arousal analysis (35),    -   body movement analysis (34),    -   95% spectral edge analysis (36),    -   anaesthetic phase and spectral energy variance measurement in        association with a subject's state of consciousness. (30),    -   Pulse Transient Time (PTT) based arousal detection (31),    -   PTT measure and PTT based blood-pressure reference measure (31,        32),    -   PTT based heart rate and blood pressure with simple non-invasive        oximeter (31, 32)    -   PAT analysis for sympathetic arousal detection (104-108),    -   EEG spike-K-complex-wave-activity-event categorization (47),    -   bio-blanket-heart-temperature-PTT        blood-pressure-respiration-breathing sound (49).

In addition to the above analysis techniques the HCM system of thepresent invention may access any combination of one or more of the aboveanalysis techniques concurrently and determine the:

-   -   context,    -   physiological vigilance or sleep or wake or consciousness        transition; and    -   predict “probability of transition” of a subject's vigilance        state.

The “context and predictive” analysis includes providing a validation ofthe subject's sleep or hypnotic state by referencing a combination ofthe above analysis techniques in terms of the current vigilance phaseand a trend or sequence vigilance phase. If, for example the HCM systemdetermines that the subjects current vigilance state does not qualifyfor classification under conventional rules as depicted by R&K analysis(34), but was detected by way of BIC coherence analysis (33) asprogressing to a deeper stage of hypnotic state or a deeper state ofunconsciousness (as with deeper state of in-depth anaesthesia state),then the HCM system may make a more accurate decision based onpredictions from the context of the R&K and BIC analysis past andcurrent trend data. In this particular case the prediction may be thatthe subject is entering a phase of deeper unconsciousness or hypnoticstate (by way of no R&K state and BIC analysis), and accordingly has ahigher probability of predicting that the subject is more likely to beapproaching a transition of unconsciousness to consciousness. Thisaforementioned prediction may alert the HCM system that the mostaccurate method of analysis in the phase from unconsciousness toconsciousness is likely to be Auditory Evoked Potential response. TheHCM system present may “self-adapt” the analysis method in accordance tothe sequence of the subject's vigilance state transitions in order toprovide improved accuracy for monitoring a subjects vigilance or to moreappropriately classify same into a sleep, hypnotic or consciousnessstate of the subject being monitored. “Self adaptation” in this contextrefers to the capability of the HCM system to initially weight vigilanceanalysis towards BIC as the preferred method for analysing a subject'stransition from wake to unconsciousness, and then subsequently weightAudio Evoked Potential response as the preferred method of analysing apatient's transition from unconsciousness to consciousness.

The HCM system of the present invention may determine the most probabletransition states by evaluating the trend or sequence of data outputfrom more than one analysis type. Example of vigilance transition statesinclude:

-   -   consciousness to unconsciousness    -   unconsciousness to consciousness    -   sleep to wake    -   wake to sleep    -   deepening of unconsciousness (or hypnotic) state    -   exiting of unconsciousness (or hypnotic) state

Examples of analysis types that may be automatically allocated based ona subject's current vigilance transition state and current stateinclude:

AUTOMATIC PREFERRED TRANSITION STATES ANALYSIS TYPE Consciousness tounconsciousness BIP Unconsciousness to consciousness AEP Sleep to wake1)R&K, 2)BIC Wake to sleep 1)R&K, 2)BIC Deepening of unconsciousness (orBIC hypnotic) state Exiting of unconsciousness (or AEP hypnotic) state

AUTOMATIC PREFERRED ANALYSIS CURRENT STATE TYPE FOR STATE CLARIFICATIONConsciousness or wake 1)BIC, 2)R&K Unconsciousness AEP Sleep state R&KWake state or consciousness 1)BIC, 2)R&K

The HCM system of the present invention may take into account theinstantaneous and trend analysis outputs from one or more analysis typeto determine a subjects most probable transition state and may thenselect the most qualified or accurate analysis type as the primarydecision weighting of a subject's state of consciousness (hypnoticstate), wake, sleep or vigilance.

The HCM system of the present invention may include a learningcapability and pattern recognition to enable different combinations ofanalysis type and different combinations of trends of analysis, todetermine the most appropriate analysis type for determining thepatient's vigilance.

Furthermore the HCM system of the present invention may recognisecombinations of analysis output to improve accuracy of detecting asubject's vigilant state or transition of the subject's vigilant state.

The HCM system of the present invention may apply both FFT and ½ periodamplitude analysis in consecutive 1 second intervals (can be set togreater values, particularly where lower frequency responsecharacteristics are being utilized). The FFT analysis (i.e. 95% spectraledge (36)) has an advantage of providing power distribution of the EEGsignal frequencies but the disadvantage of not presenting mixedfrequency EEG signals for assessment under scoring criteria such as perR&K analysis EEG (34, 45, 46). An example of where ½ period amplitudeanalysis may provide an advantage over frequency analysis is where a 30second epoch contains a high amplitude Delta wave and the Delta wavedoes not constitute greater than 50% of the 30 second epoch, but due toexcessively high amplitude of the Delta wave, would appear to dominatethe 30 second epoch. In this case use of FFT would suggest that thisepoch is, say stage 4 (greater than 50% of the epoch time with highamplitude Delta wave in accordance with R&K analysis (34, 45, 46).However if for example, the epoch consisted of greater than 50% of theepoch in Alpha EEG waves as would be more evident (than FFT analysis)with ½ period amplitude analysis then this epoch should in accordancewith R&K human sleep scoring criteria, not be scored as stage 4 ofsleep. In other words the % period amplitude analysis more correctlyrepresents the method of scoring sleep in accordance with R&K than FFTin such instances and utilization of FFT and ½ period analysis (45) mayprovide improved accuracy for determining a subject's consciousnessstate (33) and sleep state (34) in the HCM system.

The HCM system of the present invention may include automatic InputSignal Validation, Optimisation & Compensation (ASVC) includingautomatic substitution of poor quality electrode connections (referFIGS. 17, 18,34,35,37,41,42,45). This function may enable the system toautomatically validate input signals (physiological variables in thepresent application but applicable to other industries involvingmonitoring or analysis of signals in general) of a subject's monitoredvariables. Validation may be by way of automatic impedance measurement,frequency response, mains interference, signal to noise and signaldistortion and other measured signal characteristics as part of theanalysis algorithm for monitoring, detecting or predicting a subject'sstate of consciousness, sedation or vigilance.

Furthermore the HCM system of the present invention may automaticallydetermine signal conditions during operation of the system, and invokesubsequent signal processing to compensate or reduce artefacts caused byunwanted signal distortion or interference such as noise. Furthermore,in order to allow the system to display to the user on-going signalvalidation and signal quality issues, signal status and subsequentcompensation (or signal correction), signal trends or progressivedeterioration of signal quality and existing signal quality issues, bothcurrent and trend signal status may be displayed in real-time andstored, with both modified and compensated signal data.

The HCM system of the present invention may provide trace ability (or anaudit trail) of all signal modifications so that the system user canvalidate any automatic signal compensation decisions both in real-timeand in later study review. A further feature of the HCM system is acapability to provide the user qualification, at all times, relating todetected signal deterioration and subsequent signal compensation. Afurther capability may allow the user of the HCM system to automaticallyor manually (upon the user's discretion or agreement with qualificationof signal deterioration and proposed compensation) invoke signalcompensation for optimising or improving signal quality. Due to timesynchronised (with recorded signals) trace ability (audit trail) ofsignal validation and subsequent signal compensation, modified signalsmay be revoked (unmodified) to original signal format where required.

Furthermore, signal validation may provide a means to allow the systemto optimise signal quality for improved application of varioussignal-processing algorithms.

The system of the present invention may adapt or re-assign redundant orspare electrode channels in substitution of identified poor qualitysignal channels. In particular the system may automatically alert a userof the quality of all attached electrodes and sensors. Where any poorsignal quality is detected the system may advise the user ofrecommendations or hints to quickly identify and resolve signal qualityproblems.

Surface electrode connections have been applied in the past for variousphysiological parameters and monitoring applications. However oneproblem associated with surface electrode connections is that thequality of the connection to the patient can deteriorate as a result ofa number of conditions including patient sweat, movement, or the dryingout of the connecting electrolyte solution between electrode andpatient. In particular the problems of electrode quality may beparticularly critical in applications such as those associated withintensive care and operating theatre environments, as is the case withdepth of anaesthesia monitoring systems.

To the applicant's knowledge, no one has used connection of redundantelectrodes, automatic electrode connection quality and validation by wayof routine impedance measurements and other signal validation techniques(refer FIG. 18—MFD Block 7) and automatic substitution of poor electrodeconnections with the redundant (spare) electrode connections (refer FIG.35 (IAMES) or 37 ISES)). The system of the present invention may utiliseredundant electrode systems together with integrated electrode-sensorsand wireless/rechargeable electrode-sensors to minimize the quantity ofelectrodes and sensors (as few as 3 sensor-electrodes) for depth ofanaesthesia monitoring (where the quantity, reliability and simplicityof electrode-sensor attachments is very critical) and analysis includingmind-state, auditory sensory, visual sensory, arousal sensory, anxietysensory and vital signs physiological states.

The system of the present invention may include automatic AnalysisValidation, Compensation, Optimisation, Adaptation of Format andAnalysis and Probability Assignment (AAVCOAFA)(refer FIGS. 16,17,18,34,35,37,41,42,45). The system may adapt algorithms fordetermining the subject's state of consciousness (and vulnerability toanaesthesia procedure recall) while simultaneously in substantivelyreal-time allowing the system to determine and display to the user thesignal analysis methods being deployed (such as R&K derived fromoptimised BIC—outer malar bone surface electrodes—as opposed to C3 EEGsignal) signals status, trends or progressive deterioration of signals(such as detailed in (AVCOADSP), or analysis quality caused by, forexample, input signal connection deterioration, or connection ofimproved signal inputs. In other words the system may determine the mostappropriate (accurate and reliable) analysis method (algorithm type) byway of validating input signal quality and automatically or manuallyactivate a changed analysis method or format that is the most suitablefor the validated signal channels. The analysis methods may bedetermined according to presence, status and quality of the patientsignals being monitored.

A further capability of automatic analysis validation is that the systemmay adapt or re-assign variants or substitute analysis formats where theexisting analysis format requires change, such as when an input channelconnection(s) deteriorate.

The system may automatically alert the user of the quality andprobability of the applied analysis processes. The system may alsoadvise the user of recommendations or hints to quickly identify andresolve analysis validation deterioration or issues.

The HCM system of the present invention may display to the user on-goinganalysis validation status, progressive deterioration of analysisquality and subsequent analysis variation or analysis compensation dueto signal deterioration, for example.

Furthermore once analysis types have been activated, weightingtechniques may be applied in order to determine the probabilityassociated with different analysis methods. For example, BIC (outermalbar bone, surface electrode placement) derived R&K EEG analysis doesnot produce as high a probability as C3 (surface electrode) derived R&KEEG analysis.

The HCM system of the present invention may provide an automaticanalysis format linked to signal validation, such as in the case ofsleep and wake analysis where the analysis parameters applied may dependon the validated signals. If, for example, only EEG outer malbarelectrodes are validated, then frequency optimised EEG outer malbarsignals may be utilized for analysis, as opposed to more complexanalysis signal combinations including EMG and EOG signals.

The system of the present invention may include Patient Data-LinkedAnalysis (PDA)(refer FIGS. 16,17,18,34,35,37,41,42,45). The system mayadapt the analysis algorithms used for determining a subject's state ofconsciousness (and vulnerability to anaesthesia procedure recall) inaccordance with critical data such as the subject's body mass index(weight, height), sex and age. Such Patient Data-Linked (PDA) analysismay enable patient specific data such as the subject's body mass index,age, sex, medical history and other relevant information to be utilisedin analysis algorithms for monitoring, detecting or predicting the stateof consciousness, sedation or vigilance of the subject.

Patient specific data is entered in prior art patient monitoringsystems. However in some instances patient specific data cansubstantially affect monitoring or analysis methods associated with themonitoring system. To the applicants knowledge, no one has linkedcritical parameters such as weight, age and sex of a patient to thesensitivity and weighting of depth of anaesthesia monitoring. The HCMsystem of the present invention may change the weighting or sensitivityof analysis of the physiological parameters being monitored. An exampleof this is where the weight or sex of a subject affects (in accordancewith empirical clinical data), the optimal band of operation of a givenconcentration of an anaesthetic agent, due to the effects that sex andweight have on these parameters.

The HCM system of the present invention may utilise certain patientdata, which may vary the sensitivity or important thresholds associatedwith variations between one patient and the next. The “utilisation” ofthis data refers to compensation, for example, of critical displaythreshold levels and sensitivity of various user displays. These userdisplay thresholds and associated sensitivity variations may change inaccordance with critical applications, for example when using the systemto monitor sensitivity of depth of anaesthesia to certain anaestheticagents.

Table A below shows one example of Patient Specific Data EntryParameters:

TABLE A PATIENT SPECIFIC INPUT DATA Age: Weight: Height: SEX: BMI:History file: Calibration file: Calibration-file anesthetic type:

The system of the present invention may include Calibration-LinkedAnalysis (refer FIGS. 16, 17,18,34,35,37,41,42,45). The system may adaptthe analysis algorithms used for determining a subject's state ofconsciousness (and vulnerability to anaesthesia procedure recall) inaccordance with the subject's critical calibration data, such as how thesubject responds to various preliminary or pre-test studies. This“calibration data” may include thresholds and parameters derived from aspecific patient's preliminary study, in order to determine thecharacteristics of the subject's physiological parameters for moreaccurate consideration of variations between different subjects.

This capability may be important where, for example, a subject undergoesa critical operation. To minimise the risk associated with anaesthesiaadministration, a preliminary calibration study can be conduced. Thisstudy may include a capability to store tables of values or specificdrug administration versus analysis state (BIC/AEP/R&K/95% spectral edgeor other) coefficients or specific analysis values associated withvarying degrees of drug administration.

The system of the present invention may include localized or generalmotor and sensory nerve and muscle response and arousal analysis (referFIGS. 16, 17,18,34,35,37,41-45). The system may adapt algorithms usedfor determining a subject's state of consciousness (and vulnerability toanaesthesia procedure recall) in accordance with monitoring anddetection of the subject's arousals (typically detected from shifts infrequency and amplitude in monitored signals) or muscle responses (forexample during an operation or medical procedure). The system may applythis data as an alert or detection means for the subject's transitionstate or physiological and mind-state response to a medical procedureand a means of consciousness state detection. In other words the musclechanges or arousal events may be indicative of muscle responses of thesubject, which in turn may indicate the subjects localised anaesthesiaeffectiveness or the subject's state of consciousness and local muscleresponse.

In particular localised monitoring and detection of muscle movement oractivity may provide a means to localise the arousal and musclemonitoring, relative to the responsive or sensitive areas associatedwith a medical procedure, and consequently may provide immediatefeedback where an anaesthetised area of a subject indicates muscle ornerve responses consistent with inadequate anaesthetic drugadministration. The system may include accurate monitoring and recordingof the effect of local anaesthetic by detecting the subject's motor andsensory responses in conjunction or time-linked with an incision orother medical procedure. The latter feature may provide a means ofmonitoring and analysing both the state of a subject's mind and theresponse from selected ear related (cochlear) procedures where asubject's state of anaesthesia and performance or response of theauditory system can be monitored and analysed throughout an operationprocedure. Industry standard techniques (for example, Canadian TaskForce)(35) for detecting arousals may be utilized in the system of thepresent invention.

The HCM system of the present invention may include an electricalstimulus pulse (evoked potential) and test of the nerve or muscleresponse of a subject while undergoing an operation or medicalprocedure. The electrical stimulus pulse may be applied at a selectedexcitation location on the subject's body, and the response (nerve ormuscle) can function in a dual-monitoring mode whereby determining thesubjects state of consciousness or vigilance (as in depth of anaesthesiamonitoring) and determining the response and performance of selectedmuscles or nerves of the subject may be performed simultaneously. This“dual-monitoring” function may be particularly useful when a subject isundergoing a delicate and precise medical operation or procedure.

The system of the present invention may include an IntegratedAnaesthesia Monitoring Electrode System (IAMES)(refer FIGS. 16,17,18,34,35,37,38,41-45). IAMES may be wired or wireless. IAMES mayinclude a simple, low cost and minimally intrusive electrode system,which may be disposable or reusable with a connector interface to areplaceable EAS. Alternatively EAS may be integrated with a WirelessElectronic Module (WEM). A version which is completely disposable wouldtypically be lower in cost and may not in some lower cost options,include a wireless interface. The lower cost completely disposableversions may include a low cost data logging system with low costdisplay means. Low cost display means for completely disposableversions, may include once of display output for index measure, forexample, or digital interface or data card for information retrieval.

The IAMES system may be divided into two components including anelectrode attachment system (EAS) and the WEM section. Completelydisposable systems may include integrated WEM and EAS sections forfurther cost reduction.

The EAS system is a remote patient attached electrode transceivermonitoring station, which contains a means of inputting patient data tothe WEM module (refer below). EAS includes a code identification systemallowing system configuration to be set up in accordance with thespecific electrode type (ie. EEG, EOG, EMG, EEG or other).

EAS includes conductive surfaces which may be easily attached to apatient's skin surface for electrical pick-up of physiological variablesassociated with a subject including a combination of left and right,outer malbar placed electrodes for detecting typical bicoherence EEGvariables, left and right outer carantheous eye electrodes for detectingEOG electrical signal associated with eye movements, chin sub-mental EMGelectrodes for detecting the subject's chin muscle activity and state ofrestfulness, A1 or A2 electrodes (dependent on the format of theelectrode system) for providing an electro-physiological referencesignal and eye lid position sensors for detecting eye opening activityand percentage of eye opening.

A combination (hybrid) system may provide R&K and/or bicoherence signalattachment in one wireless hybrid device, thus opening up avenues forlarge scale home monitoring of sleep disorders, more criticalapplications such as medical procedures and operations or vigilancemonitoring of workers or air/land/sea transport personnel. Options mayinclude sub-mental EMG and/or auditory sound output devices (ear-piece,headphones or speaker) and/or auditory signal pick-up devices (surfaceelectro-physiological electrode).

A Wireless Electronic Module (WEM) system may include a small, low powerand lightweight module designed to snap connect to an EAS module. TheWEM module may provide the following functions:

-   -   interface for one or more channels of patient data emanating        from the EAS module;    -   electrode and sensor amplification (DSP and/or analogue        methods);    -   filtering (DSP and/or analogue methods);    -   calibration testing including generation of one or more        (different wave-shapes, frequency and amplitude) local test        waveforms;    -   impedance measurement;    -   signal quality measurement;    -   input DC offset measurement;    -   wireless data transceiving and DSP or micro-controller data        processing capabilities; and    -   reference code identification detailing electrode type (eg. EEG,        EOG, EMG, EEG or other).

The WEM transceiver module may transmit physiological signals andvarious test data such as the impedance value across the electrodesignals, quality measure of signal or data such as a reference codedetailing electrode type (ie. EEG, EOG, EMG, EEG or other). The EAStransceiver module may also receive various control and test commandssuch as requests to measure impedance, generation of test or calibrationwaveforms, a measure of signal quality and other data.

The WEM system may be powered via any combination of rechargeable orsingle use batteries, self powered electrodes with a capability ofcharging via RF or EMF induction during use or as a charging procedure.

A WEM module may be directly attached to an EAS module, or it may beattached to an EAS module via an intermediate wireless link or wiredattachment. Alternatively, patient worn or patient attached device(s)such as headband, head-cap or hat, wrist-worn or other devices mayincorporate an EAS and/or WEM module.

The WEM module may be self powered with Radio Frequency orElectromagnetic frequency providing a power supplement. The lattersystem may utilise radio or electromagnetic signals as a means forrecharging the power source in the WEM module.

The IMES device may be wirelessly linked to close proximity or distantmonitoring systems equipped with a wireless data interface capability toIMES. Close proximity monitoring devices may include the headrest of acar seat where a self-powered IMES system (typically EMF power rechargesystem) may be wirelessly linked to a transceiver device containedwithin the driver's seat headrest or other convenient or appropriatelocation(s). The WEM may be wirelessly linked to remote computer deviceswherein WEM data may be stored, displayed and/or analysed. The remoteWEM device may also provide a controlled interface to the WEM module forcalibration and impedance testing. WEM may also be wirelessly linked tomobile phones or wireless modems or a network interface including anInternet connection.

The IMES device, when incorporated with local (incorporated in WEMmodule) or remote (wireless or wire-linked) BIC analysis may provideanalysis for detecting vehicle or machine operator vigilance with awireless electrode option.

The system of the present invention may include an Eye Opening Sensor(EOS)(refer FIGS. 34, 35,37,42). The EOS system may provide an improveddevice for sensing and measuring Eye Opening. Eye movement sensors (suchas piezo or PVD movement sensors) and electrodes (such as EOG) have beenused in the past for detecting eye movement or eye-lid movementrespectively. However one problem associated with depth of anaesthesiamonitoring is the fact that some patients awaken prematurely during amedical procedure and opening of the eyes can lead to distressing viewsand later recall or nightmare occurrences. A further problem is thepatient may litigate in such instances. An objective and accuraterecording of the patient's state and amount of eye opening is thereforedesirable. A system that allows the user to calibrate such aneye-opening sensor may also be of value. The HCM system of the presentinvention may include such a sensor (refer FIG. 34) for detecting in acalibrated manner the degree of opening of a subject's eye.

The EOS system includes an eyelid position monitor and an EOG sensor.The EOS system may include conventional surface electrodeelectro-physiological signal sensing in conjunction with a capability todetect the position of a subject's eyelid at any point in time. Combinedsensing of eye movement and eye opening may provide a simple, minimallyinvasive sensing system ideally suited to a subject's eye region toprovide eye blink details and rate, eye open percentage and eye movementinformation. The sensor can be wire or wireless connected to amonitoring system. The EOS system may also be provided in an embodiment,whereby EOG sensing is achieved within the same sensor attachmentsystem. Special design variations may provide simple self-appliedsensors, which can be safely and easily applied in a manner similar toattaching a band-aid.

A further option exists using self-applied electrodes where theelectrodes may include a low cost disposable component and a moreexpensive reusable component. For example the connector and electronicscircuit may be reusable, while the applied section of the sensor may bedisposable.

The HCM system may also provide an improved capability for calibratingeye position at commencement or at any stage during a subject's use ofthe EOS sensor. Calibration may be applied by determining (measuring,storing and determining calibration data versus corresponding eyeopening status) the output of the EOS sensor under varying conditions,eg. by asking a subject to close their eyes, and storing the respondingEOS signal. The EOS system may incorporate the format of the WEM and theEAS.

The system of the present invention may include an Integrated SleepElectrode system (ISES)(refer FIGS. 35, 37,42). The ISES device mayprovide a self-applied electrode system for sleep/wake analysis of asubject. The electrode system may attach outer malbar or any two EEGelectrodes to a subject's forehead as part of a monolithic self-adhesiveand self-applied electrode system. An analysis method may be applied tothe ISES device's signal output to provide sleep/wake or bicoherenceanalysis. A flexible insert may facilitate elasticity to accommodatedifferent patient sizes. Electrodes may include varieties including anattachable version and disposable dot surface re-usable electrodes (suchas from 3M) and reusable/disposable electrodes. The ISES system mayinclude the format of the Wireless Electrode Module (WEM) and theElectrode Attachment System (EAS).

The system of the present invention may include a user programmabledevice with real-time display of integrated analysis index andincorporating at least two weighted and combined modes of analysis(refer FIGS. 16, 17,18,34,35,37,41-45). The apparatus may include acapability to output one or more analysis algorithms including acombination of simultaneous, real-time analysis of R&K analysis (34),AEP (30), spectral analysis-SEF-MF (4), Bi-coherence (BIC) analysis(33), initial wave analysis (5), auditory response (30), arousalanalysis (35) and body movement analysis (34), 95% spectral edgeanalysis (36) and anaesthetic phase and spectral energy variancemeasurement in association with the subject's state of consciousness(30), Pulse Transient Time (PTT) based arousal detection (31), PTTmeasure and PTT based blood-pressure reference measure, Pulse oximetrySA02, PTT based heart rate and blood pressure with simple non-invasiveoximeter, PAT analysis for sympathetic arousal detection (104-108), EEGspike-K-complex-wave-activity-event categorisation (47) and bio-blanketheart-temperature-PTT blood-pressure-respiration-breathing sound (49).The specific types of analyses can be determined by way of signalvalidation, user's selection of analysis requirement (such as depth ofanaesthesia, vigilance, sleep-wake and other) and electrodes input tothe system.

The HCM system of the present invention addresses the limitation of theprior earlier art by presenting a simple mode of display to the userwhich represents a simple measure of the subject's current state ofconsciousness or hypnotic state. This particular aspect of the HCMsystem may communicate to the end-user a simple measure of the subject'sconsciousness despite a vast range of complex analysis measurements, asdetailed herein. In addition to providing a simple overall measurementand display method the HCM system may also provide a means of storingand displaying all recorded raw data and outputs of each analysis methodfor complete system verification and trace ability relating to anydisplay of conscious or vigilant state of a subject. The raw data andanalysis data may be stored and available for later review, reportingand printing, as is required from time to time to verify systemperformance and operation.

The HCM system of the present invention may improve accuracy ofprediction of the state of consciousness, or a subject's vigilance bycomparing actual EEG amplitude variations with predicted EEG amplitudevariations where predicted EEG behaviour may include predictions of EEGamplitude variation during anaesthesia drug administration against depthof anaesthesia prediction (29)(refer FIGS. 16, 17,18,34,35,37,41-45).The HCM system may recognize EEG amplitude variations associated withphysiological phenomena such as EEG bursts as opposed to EEG amplitudevariations associated with movement or other forms of artefact, such asexcessive beta frequencies.

The HCM system of the present invention may apply amplitude analysis tothe EEG signals. By analysing monitored EEG amplitudes from a subjectand comparing this signal to a pre-known amplitude trend or signalbehaviour, it may enhance accuracy of prediction of anaesthetic drugadministration. The “pre-known” behaviour trend may provide a means topredict the state of the depth of anaesthesia by referencing a known orpredicted sequence or trend of EEG amplitude variation (behaviour) withthe subject's actual EEG amplitude or patterns of EEG amplitudevariation whilst under sedation or anaesthesia, for example.

The HCM system of the present invention, may reference amplitude trendpredictions and signal modelling such as described by Moira L.Steyne-Ross and D. A. Steyne-Ross, of Department of Anaesthetics,Waikato Hospital, Hamilton, New Zealand (29) in a paper entitled“Theoretical electroencephalogram stationary spectrum forwhite-noise-driven cortex: Evidence for a general anaesthetic-inducedphase transition”. This paper describes an increase in EEG spectralpower in the vicinity of the critical point of transition intocomatose-unconsciousness. In similar context to the above-mentionedweighting methods, the HCM system of the present invention may weightthe analysis output from amplitude analysis of the EEG signal. The EEGanalysis may include comparison of actual monitored EEG signal andtrends and predicted signal or trend associated with the subject'stransition from consciousness to consciousness and visa versa.

The output of amplitude processing may be input to a weighting table forfinal consideration in the monitoring, detection and alerts associatedwith depth of anaesthesia, hypnotic state, sedation depth, fatigue orvigilance of the subject.

The system of the present invention may include a Programmable ElectrodeInterface System (PEIS) (refer FIGS. 16, 17,18,34,35,37). The PEISapparatus may provide a means for intuitive user guidance and operation.The user of the HCM system can select a desired function (for exampledepth of anaesthesia monitoring, vigilance monitoring, sedationmonitoring) and the system may illuminate by way of LED, LCD or otherdisplay system, the required electrode connections and recommendedposition on subject such as the location of various surface electrodes.

The PEIS apparatus may provide a prompting capability, indicating to theuser, which electrodes require attention, eg. surface electrode mayrequire re-attachment due to excessive impedance.

In a preferred embodiment the PEIS apparatus may include a touch screenprogrammable electrode attachment guidance system.

The system of the present invention may include a Biological BlanketSensor (BBS). The BBS may enable a wired or wireless interface providinga range of measurements for assistance in determining arousal movements,body movement, breathing sounds, heart sounds, respiration, heart rate,Pulse Transient Time, Blood pressure and temperature.

The BBS apparatus may be sensitised with sensor elements whereby thesensor reacts to subject movement causing a change in impedance of aresistive element, piezo or PVD element (49).

The system of the present invention may include a Biological SensorOximeter with Integrated and Wireless-Linked ECG and Pulse TransientTime (PTT) Monitoring and Analysis (refer FIG. 33). The latter apparatusmay monitor a subject's blood pressure variation, micro-arousaldetection for detecting sleep or consciousness fragmentation(particularly useful but not limited to depth of anaesthesiaconsciousness monitoring and analysis), oximetry, temperature, ECGwaveform and analysis, heart rate variability and cardio-balistogramrespiratory monitoring output and respiratory event detection.

Prior art non-invasive blood pressure devices utilise techniques such asfinger attachment probes. These finger attachment systems apply pressureto a patient's finger and can become uncomfortable after a period ofattachment to the patient. Other non-invasive blood pressuremeasurements have been presented including qualitative methods. One suchqualitative method is a qualitative derivation of Pulse Transit Time(PTT) by means of a calculation utilising the electrocardiograph (ECG)waveform and the pulse waveform of the subject. The ECG waveform istypically derived from a chest located ECG surface electrode attachment.The pulse waveform may be derived from the plethysmograph pulse waveformof a pulse oximeter probe attachment at a location such as a patient'sfinger. The calculation for deriving qualitative blood pressure value isbased on the relationship, which exists between PTT and Blood pressure.Plethysmograph data may also be used to establish sympathetic arousalconditions (104), which may be related to stress or anxiety and whichare physiological signs of premature awakening.

However a number of patient monitoring applications require continuousand close to real-time blood pressure measures of the subject to detecta significant physiological blood-pressure change or related event.

Furthermore existing minimum invasive methods for blood-pressuremeasurement typically involve a cuff device placed around the subject'supper arm. The cuff device may be inflated and deflated to measure bloodpressure. This method of measuring blood pressure may be applied to apatient on a periodic basis. Other methods for minimally invasiveblood-pressure measurement include wristband cuffs with similarinflatable and deflateable bands. Whilst these wristband cuff bloodpressure systems, are potentially less invasive than upper arm cuff typesystems, it is apparent that measurement reliability of wrist systems ismore vulnerable to sensitivity of positioning and difficulty inobtaining a consistent and reliable measurement. Both cuff type systemsare not used routinely for real-time and continuous blood pressuremonitoring applications (such as depth of anaesthesia, respiratorydisorder and sleep disorder monitoring) due to obvious discomfort andcomplexity and inconvenience of such measurement techniques.

An object of real-time blood pressure, measurement technique is to applya 3-point wireless localised network (raw data and analysis results maybe transmitted to a remote computer, if required) to provide a minimallynon-invasive, minimally obtrusive blood pressure measurement apparatus.One aspect of this apparatus is that the clinically accepted standardfor upper-arm cuff inflation/deflation measurement may providecalibration and absolute blood pressure measurement, while the oximeterfinger (for example—another location for oximeter pulse) SAO2measurement together with plethysmograph (provides pulse waveform formeasurement of pulse transit time) and ECG surface electrode may providea reference heart signal to be used in conjunction with the oximeterfinger pulse signal to produce a calculation in real-time for pulsetransit time. Pulse transit time is recognised as a means of qualitativeblood pressure measurement (31, 32).

In contrast to the prior art the HCM system of the present invention mayapply periodic cuff attached (arm, wrist or other patient attachmentlocation) blood-pressure measurement system, in conjunction with anoximeter pulse waveform and ECG waveform (for PTT calculation). Themethod of utilising the PTT (by way of oximeter pulse wave and ECGwaveform) together with periodic cuff based blood-pressure measurementmay provide a means to derive a quantitative blood-pressure measurementfrom the cuff value, and a qualitative blood-pressure measurement fromthe PTT calculated signal. In other words the baseline quantitativeblood-pressure value may be derived from the cuff blood-pressure value,while a continuous and qualitative blood pressure value may be derivedfrom the PTT value. The benefit of this type of system is accuracy and acontinuous blood pressure monitoring capability, while maintainingpatient comfort by implementing cuff inflation and deflation only atperiodic time intervals.

Furthermore the system may simplify user operation with application ofwireless interconnection of the pulse oximeter, ECG electrode and bloodpressure cuff. Wireless interconnection may allow calculation ofcontinuous blood pressure at a remote wireless or wire-linked site (suchas a patient monitoring device), at the ECG electrode attachment site,at the oximeter finger probe site or the blood pressure cuff site.

The system of the present invention may include an audiovisual recalland speech sensory validation system (refer FIG. 43). The latter mayprovide audiovisual recall or replay and time synchronisation with depthof anaesthesia analysis data and raw data. Audiovisual recall mayprovide a means to correlate physiological or analysis data associatedwith depth of anaesthesia monitoring.

The audiovisual system may be configured in several options. One optionmay include a capability to store more than one audio channelsynchronised with the subject's measured physiological data. The storedand monitored (and optionally analysed or condensed) audio channel mayinclude sound or speech associated with the subject, to accommodatemonitoring and detection associated with the subject's speech sensorysystem. This function may be deployed as a last line of defence where apartly anaesthetised patient is attempting to notify the medical team,in case of partial or complete consciousness associated with potentialundesired recall of a medical procedure.

The system user may select physiological events or combinations ofphysiological events as event markers. The event markers may form thebasis of time markers pointing to significant or relevant events. Theevent markers may be associated with specific audio and/or video relatedevents. The “audio” and/or “video” related events includephysiologically related or environmental related events. Physiologicallyrelated events include combinations of or single patient data changeswhich may be related to the patient's significant (i.e. the levelexceeds a certain threshold condition) or relevant (to the users or thesystem's programmed detection threshold) changes in consciousness state.The system's time synchronisation between video, audio, physiologicaldata and analysis data may provide a means for audio and video to berecalled and analysed in conjunction with the subject's state ofconsciousness as indicated by the status of eye opening, AEP, arousal,bi-coherence analysis, and other analysed states.

One example of an audio and/or video “relevant” event may be where athreshold level (user set or system default set) is exceeded indicatinga potential for onset of consciousness. Detection that the audio evokedthreshold is exceeded may be linked to detection of “environmental”and/or system generated audio threshold being exceeded, where“environmental” audio denotes audio recorded in the operating theatrefrom music, speech or other sources of noise. “System-generated audio”refers to the audio stimulus click, which may be applied to thepatient's ear or ears during an operating procedure.

The system may detect incidence of exceeding a preset environmentalaudio threshold in conjunction with a physiological event such as audioevoked potential amplitude exceeding a certain threshold condition(typically a certain averaged amplitude measured with a certain timedelay from a trigger point). This “capability” may provide an efficient(subject to system or user threshold programming) method for validatingor evidence of a likely incidence of audio recall associated with aprocedure involving application of anaesthesia. The system may presentin a condensed graphic or numeric form an association between thesubject's hearing status (as detected from an audio sensory nervemonitoring signal) associated with incidence of environmental sound (asdetected from the recording of audio within the operating theatreenvironment). This “association” may allow the system user toefficiently investigate correlation of a patient's hearing response andactual alleged audio recall. For legal purposes this facility may detectwhether a subject's audio sensory nerve was indeed active (as opposed toinactive during an unconscious state) and whether the alleged audiorecall of specific music or words was indeed probable. The“environmental audio” recording may be achieved by means of a patientattached microphone, such as a microphone attached to an outward side ofthe patient's earpiece or headset speaker system (as applied forgenerating an audio click for Auditory Evoked Potential). This type ofmethod has an advantage of providing a dual-purpose sensor/speakersystem, while also providing specific and directional audio pick-upassociated with the patient's hearing system.

Similarly, where a subject claims visual recall during an operation, anappropriately placed theatre camera that is time synchronised withphysiological data and analysis may record the alleged vision. Visionrecall may be compared to Systems detection (manual, automatic orcomputer assisted) of a subject's eye opening for example. For legalpurposes this facility may detect whether a subject's alleged visionrecall was indeed possible as opposed to impossible, such as when thepatient's eyes are both closed.

In other words, the system may allow audio validation—i.e. if thesubject's AEP data indicated that the alleged audio recall wascoincident with inactive auditory evoked potential, for example, thismay support data for medical defence against audio recall operationclaims. Similarly video of the patient could disclose whether or notvisual recall claims coincided with patient eye open status.

In another example, bi-coherence analysis of importance such as wherespecific threshold conditions are exceeded may be validated by reviewingit in a time-synchronised format with video and audio recorded during asubject's operation. This validation may allow quantitative data tosubstantiate claims such as audio or visual recall associated with anoperation procedure.

The system may optionally include means for recording the subject'staste (some patient's claim taste recall, such as taste which may beassociated with anaesthetic gas delivery), utilising taste biochips andagain providing an association between consciousness state physiologicaland analysis parameters with taste and/or physiological taste sensors.In some cases the medical specialist may deem monitoring of taste sensorsensory system status as a requirement.

A further option may be to use two simultaneously acquired images, whereeach image is acquired at a different wavelength of light. Reflectionsfrom the patient's face may then be identical except for reflections ofthe eyes. By subtracting these two images, a third image consisting ofthe subject's eyes may be created. Finally, the image of the patient'seyes may be measured to provide a non-invasive and non-obtrusive measureof eyes opening and blink rates of the subject (99). This data utilisingPERCLOS methods may be used as a relatively reliable measure in the HCMsystem, to ensure that a subjects eye openings particularly when thesubject should be anaesthetised and unconscious (100).

The eye opening value may provide a simple measure of the percentage ofeye opening of a patient and may clearly indicate risk of visual recallor potential awakening of the subject, during an anaesthesia procedure.

The system of the present invention may include a patient alarm alertsystem for limb-controlled alarm (refer FIG. 44). The HCM system mayinclude a wire or wireless remote device connected to or accessible byany patient limb or other location near or attached to the patient'sbody. This remote device may contain at least a means for detecting oralarming system users or healthcare workers that the patient is indistress or requires attention. This remote device may allow the subjector patient a form of “final line of defence” to premature wakening orconsciousness onset. If, for example, a patient is undergoing a localanaesthetic procedure, which does not allow verbal notification of painexperience by the patient, the HCM system's remote device may allow thepatient to signal experience of pain level to the system operator(s).Various forms of pain or consciousness level notification may bepossible. One such form is where the patient is provided a simplesqueeze control such as a rubber ball, and where the pressure resultingfrom squeezing, signals pain experience and the level of such painexperience. Other forms (subject to type of medical procedure andanaesthetic application, for example) may include, for example, anattachment for detecting foot movement, eye movement or otherappropriate means of pain or consciousness signalling.

The system of the present invention may include a Wireless Electrodesystem with automatic quality verification, redundant electrodesubstitution, and minimal sensor-electrode attachment system (referFIGS. 34, 35, 37). The HCM system may provide a minimally invasivemethod and apparatus for monitoring vigilance of people, using 2 or 3(or as many electrodes as required in a given application) foreheadlocated surface electrodes, wireless monitoring connection, activeelectrode for dry electrode minimal electrode preparation, automaticelectrode impedance measurement for detecting potential electrodequality problems, redundant electrode substitution for substitutingback-up electrodes for poor quality electrode connections and dynamicsignal quality for detecting current or pending electrode problems(refer drawings).

Paths of data storage may include localised condensed data or secondary(analysis results) data storage, or remote raw data (minimal or nocompression or condensing data techniques).

A specialised identification connection system may allow automaticidentification and channel characterisation (system configuration tosuit particular channel type) for matching between electrode applicationtypes. “Electrode application” types may include ECG, EMG, muscleactivity, piezo movement detection, bi-coherence EEG, and EOG.“Characterisation” may include sample rates, data analysis types, datacondensing formats, data storage rates, data storage format, optimalpower management, and electrical and processing optimisation. Dataformat may include on-board electrode data storage, versus remotepatient worn data storage or remote linked data storage.

Characterisation may also include aliasing filter requirements,high-pass/low-pass and notch signal biological signal filteringrequirements, and calibration requirements (for DC stability and gainrequirements). A further embodiment of the system includes a low-costdisposable wireless electrode device such as may be required formonitoring sounds provided by a PVD sensor integrated with a “band-aid”style of attachment to a subject's face for monitoring the subject'ssnoring or other breathing sounds. The apparatus may include a means toincorporate the microphone sensor, amplification, filtering, storage andCPU either as a throwaway disposable system or with the more expensiveelectronics being part of a re-usable part of the apparatus. In the casewhere the apparatus is provided as a totally disposable unit, a meansfor sensing monitoring and recording and analysing the data may beprovided for in addition to a means for displaying the analysed dataresults. The means for displaying the analysed data results may includea low cost means such as a permanent graphical chemical reactionassociated with markers, coding or other visual based system.Alternatively a digital wired connection, optical connection or magneticmeans of connection may be used to download the stored data results. Adevice may provide a means for recording airflow or bruxism events (viavibration or cheek muscle electrical activity) either as a disposable orre-useable device or a combination of a disposable electrode section anda re-useable electronics and wireless section. The apparatus may includemeans to simultaneously sense (with electrodes or transducer), monitor,record and analyse bio-physiological data within a “local” (electrodedevice module) memory device, while transmitting data to a “remote”(wrist watch or remote computer device) device. The “local” device mayprovide limited storage due to size, cost and power restraints, whilethe “remote” device may provide a means of transmitting and storing lesscondensed and more comprehensive data, as may be required for clinicalor research diagnosis or validation of diagnosis.

The system may offer any combination of very low power “self-powered”system operation. Very low power operation is possible by utilisingtransmitted EMF or radio energy, from a remote source, as a means tosupply or supplement a source of power for the system. The apparatus maybe provided in a form, which is reusable or disposable.

In a form in which the electrode is disposable the device may beconfigured in a form, which can process and condense data such that thedata can be stored in the device and may display various forms of indexor output summary. This display may be in a form where the index canrepresent an amount of time detected in a sleep or wake state (could beany stage or combinations of state including REM, non-REM, stage 1,stage 2, stage 3, stage 4, wake) by means of say a pair of bi-coherenceelectrodes. Accordingly, the apparatus may record data representing thesubject's sleep efficiency or related to the subjects sleep efficiencyto inform a patient or healthcare worker whether the subject isreceiving appropriate rest or quality of rest or quality of sleep.Similarly, a combination of a wristwatch based activity monitoring (86)and wireless electrode (such as for bi-coherence electrode monitoring)to wristwatch storage and processing may provide a low cost, minimallyinvasive and potentially highly accurate means of sleep, drowsiness ordepth of anaesthesia monitoring.

The system may utilise special re-usable or disposable electrodes inconjunction with a miniature active electrode and transceiver device.

A combination of an active electrode and transceiver may provide aunique combination within the apparatus. The active electrode interfacemay provide a localised amplifier (close to or directly connected to thesubject's surface electrode contact) to reduce stringent electrodeapplication requirements of conventional electrode systems. The factthat the electrode amplifier is relatively close to the electrode (andthus the electrical signal derived from the said subject's skin surface)avoids noise pickup normally associated with conventional electrodes.Conventional electrodes have wires of up to 1 metre length, with theelectrode amplifier being located some distance from the end of thiswire. By buffering or amplifying the patient electrode directly at thepoint of patient skin surface attachment, a higher impedance may beused. Conventional (passive) electrode systems, on the other hand, havelonger wires connected between the electrode and the electrode amplifiercreates a pick-up zone for external noise. Accordingly, a lowerelectrode impedance is required to minimise this otherwise largeexternal noise and artefact interference. An example of the benefits ofan active electrode system in this application is that the driver of avehicle may apply an electrode to his/her forehead with little or nopreparation, similar to the application of a band-aid.

An electrode application with little or no preparation may result in animpedance of say 40 K to 100 K (thousand) ohms, as opposed to a wellprepared (thorough skin cleansing and some-times light abrasion) or“conventional” electrode application impedance which would be typically5 K-10 K ohms impedance. A 40 K to 100 K ohms impedance would result insuch large interference (in conventional passive electrode systems) thatthe desired monitored physiological signal could be rendered useless orunusable, while in an active electrode system a 40 K to 100 K ohmsimpedance could produce acceptable results.

A wireless protocol may include a capability to continually scan for newdevices and allocated bandwidth requirements to accommodate incrementalor decremental demands upon the number of system channels and aggregateddata bandwidth requirements. Similarly, where system bandwidth hasreached or approaches system limitations, the user may be alerted. Inthis way the physiological electrode wireless system is a virtual plugand play system, with simple and user friendly application. The wirelessprotocol may also manage functions such as relaying both physiologicaldata and commands for continuous electrode impedance checking,calibration validation or associated adjustments, signal qualitychecking and associated adjustments, electrode substitution and otherfunctions.

The system may include Spread-spectrum based wireless, active electrodesystem suitable for in-vehicle EEG monitoring and depth of anaesthesiamonitoring amongst other applications (refer FIGS. 33, 34, 37, 42, 45).

Utilisation of an active electrode system for vigilance in-vehiclemonitoring, in conjunction with a wireless and battery or self-poweredelectrode system, may provide a self-applied driver vigilance electrodemonitoring system. In one embodiment, for example, a driver could applya self-adhesive active wireless linked forehead electrode system.

The electrode system may include a re-usable section that contains themore expensive active electronics and wireless circuitry, and adisposable section that contains the surface electrodes and some form ofinterconnection to the re-usable section. Such apparatus may be suitablefor a minimally invasive in-vehicle vigilance system where (for example)a wireless electrode monitoring device such as a forehead attachedwireless electrode system may be optionally input to an existing driverdrowsiness measurement system. In this manner a driver may choose toincrease reliability of driver drowsiness detection by using minimallyinvasive EEG bi-coherence signal monitoring and analysis. This type offunction may supplement or replace other on-board vehicle real-worlddriver drowsiness monitoring technologies associated with measurement ofdriver-movement and activity sensors (Burton, 1999) and eye openingmeasurement.

The system of the present invention may include physiological data timedelay and analysis time lag compensation. The latter may be applicablewhere anaesthesia drug administration can be monitored in real timeagainst actual display changes and the apparatus is able to predictchanges instantly for the user to avoid over or under drugadministration associated with natural hysteresis or delay factors suchas delay between the instant of drug administration and the human body'sphysiological parameters (as monitored by the apparatus) responding tothe drug administration.

The latter is applicable to parameters such as oxygen saturation wherethe physiological data reading is typically delayed by between 15 and 20seconds due to the nature of the monitoring method and the body's timedelay in blood-oxygen colour change.

The system of the present invention may include a Biofeedback loopproviding automatic anaesthesia drug rate or concentration of delivery(refer FIG. 48).

The HCM system may interface to various types of drug delivery systemsto provide varying degrees and types of biofeedback control affectingthe drug administration process. The drug delivery systems may includebut are not limited to gas ventilation or ventilation or gas deliverysystems, drug perfusion systems, amongst other drug delivery systems.“Varying degrees” of drug delivery may include a capability to limitdrug delivery or provide degrees of drug delivery or drug deliverymixture in accordance with predetermined monitoring or analysisparameters associated with the HCM System.

The system of the present invention may include a Wireless PatientElectrode Identification and Characterisation function (IDCF). Thisfunction may provide a means for the system to automatically identifythe electrode type selected by the user. Automatic identification may beby way of wireless module scanning or electrically interfacing to someresident data (contained on the disposable or reusable sensors orelectrodes, which are attached to the subject) or optical or magneticcode sequence, where a unique code is associated with each uniqueelectrode type. Various electrode types may be identified for groups ofphysiological variables, which share the same characteristics andprocessing requirements. If a user selects an ECG electrode for example,the IDSC may alert the system of optimal gain, signal range filterconditioning, aliasing filter values and types, sample-rate and databandwidth requirements for the wireless module interface, processing,acquisition, analysis, display and other functional requirements relatedto the electrode channel type.

This automatic identification system may greatly simplify systemapplication and minimise potential user errors. An example of anapplication and embodiment of this system may be where a nurse applies aseries of clearly labelled electrodes and the rest of the systemoperation is automatically configured as the patient is wired up inaccordance with the selected electrode types.

The IDCF is also useful if the application for the wireless electrodesystem is a wireless EEG electrode system that is self-applied to avehicle driver's forehead for simple “fool-proof” EEG signal monitoring.The combined application of the wireless module with automatic signalcharacterisation in accordance with detection of the electrode type,active electrode signal handling and later analysis techniquesincorporating BIC (including bi-coherence and bi-spectral analysis) mayprovide a unique wireless, artefact reduced and precise method forin-vehicle or other application of cognitive performance orvigilance/fatigue monitoring.

This function may be particularly useful for depth of anaesthesia or avehicle based vigilance system where the user needs to have a systemthat is as minimal and “fool-proof” as possible.

The IDCF system may also help to ensure that only known re-usable ordisposable electrodes are used with the system and that optimalcharacterisation and system set-ups are automatically applied inaccordance with the selected electrode types.

SUMMARY OF THE INVENTION

The HCM system of the present invention may provide improved accuracy inmonitoring, analysis, detection, prediction, system alerts and alarmsassociated with, inter alia, depth of anaesthesia, depth ofconsciousness, hypnotic state, sedation depth, fatigue or vigilance of asubject, with as few as 3 surface electrodes. The HCM system mayincorporate real-time phase, amplitude and frequency analysis of asubject's electro-encephalogram. The HCM system may provide a means toweight the output of various types of analysis and produce a combinedanalysis or display for precise indication or alert to various users ofthe system.

In particular the HMC system may monitor, store and display two or moresets of physiological data parameters or analyse one or morecombinations or calculations associated with the data to display, store,condense and summarise data for a range of applications associated withmonitoring human consciousness. The HMC system may analyse two or moreof the physiological data to produce condensed data summaries, orindexed data (such as arousals per hour and other indexes) or tabularand graphic displays and reports associated with monitoring humanconsciousness. The HMC system may correlate two or more sets of thephysiological data or analysis results to produce tertiary analysisresults associated with monitoring human consciousness.

The HMC system may be applied to monitoring depth of anaesthesia foroptimal administration of anaesthetic drugs, to sedation in tracking thesubject's level of sedation for nurses or other medical professionals,to monitoring fatigue and hypnotic state for drivers, to monitoringvigilance for transport and machine workers and to controlling deliverysystems for administering therapeutic treatment such as drugs, gas orthe like to the subject.

The HMC system may weight the outputs of one or more analysis algorithmsincluding combination of simultaneous, real-time analysis of R&Kanalysis (34, 45, 46), AEP (30), spectral analysis-SEF-MF (30),Bi-coherence (BIC) analysis (33), initial wave analysis (5), auditoryresponse (30), arousal analysis (35) and body movement analysis (34),95% spectral edge analysis (36) and anaesthetic phase and spectralenergy variance measurement in association with a subject's state ofconsciousness (29), Pulse Transient Time (PTT) based arousal detection(31), PTT measure and PTT based blood-pressure reference measure, Pulseoximetry SAO2, PTT based heart rate and blood pressure with simplenon-invasive oximeter (31,32), PAT analysis for sympathetic arousaldetection (104-108), EEG spike-K-complex-wave-activity-eventcategorisation (47) and bio-blanket for monitoring of heart,temperature, respiration (49), breathing sound and PTT blood-pressure.Inclusion of sympathetic arousal may provide a unique measure of stressor mental anxiety, despite the state of a patient's state of paralysisor “apparent unconsciousness”.

According to one aspect of the present invention there is provided amethod of monitoring consciousness of a sentient subject andautomatically detecting whether the subject is in a transition from aconscious state to a less conscious state or vice versa, by reducingeffects of frequency based changes in neurological data from thesubject, said method including:

-   -   (i) obtaining an EEG signal from the subject;    -   (ii) performing a frequency based analysis on the EEG signal to        obtain a frequency based signal;    -   (iii) performing a phase based analysis on the EEG signal to        obtain a phase based signal;    -   (iv) detecting by comparing the frequency based signal and the        phase based signal whether the subject is in transition from        said conscious state to said less conscious state or vice versa;        and    -   (v) providing a warning signal when said subject is in said        transition to said conscious state.

According to a further aspect of the present invention there is provideda method of processing a non-stationary signal including segments havingincreasing and decreasing amplitude representing physiologicalcharacteristics of a sentient subject, said segments including portionsin which said signal changes from increasing to decreasing amplitude orvice versa, said method including:

-   -   (i) detecting each segment by determining time instants when a        time derivative of said signal is substantially equal to zero;    -   (ii) performing syntactic analysis for each segment including        assigning height, width and error parameters;    -   (iii) identifying noise segments present in said signal by        comparing said width parameter to a preset threshold and said        error parameter to said height parameter;    -   (iv) removing said noise segments by replacing each identified        noise segment with a substantially straight line;    -   (v) sorting the remaining segments into a plurality of wavebands        based on their width parameters; and    -   (vi) classifying said signal as belonging to one of predefined        sleep states based on relative frequency of occurrence of said        segments in said wavebands.

According to a still further aspect of the present invention there isprovided a method of monitoring physiological characteristics of asentient subject including:

-   -   applying a first surface electrode to said subject to provide a        first electrical signal to a remote monitoring apparatus;    -   applying a second surface electrode to said subject to provide a        second electrical signal to said remote monitoring apparatus;    -   monitoring quality of said first electrical signal and in the        event of a degradation in said quality of first signal;    -   automatically substituting said second electrical signal for        said first electrical signal and in the event of a degradation        in said quality of said second electrical signal and in said        quality of said first electrical signal, providing a warning        signal.

According to a still further aspect of the present invention there isprovided an apparatus for processing a non-stationary signal includingsegments having increasing and decreasing amplitude representingphysiological characteristics of a sentient subject, said segmentsincluding portions in which said signal changes from increasing todecreasing amplitude or vice versa, said apparatus including:

-   -   (i) means for detecting each segment by determining time        instants when a time derivative of said signal is substantially        equal to zero;    -   (ii) means for dividing said signal into said segments including        data over three consecutive time instants when said time        derivative is equal to zero;    -   (iii) means for assigning to each segment, height, width and        error parameters;    -   (iv) means for identifying noise segments in said signal        including means for comparing for each segment said width        parameter to a preset threshold and said error parameter to said        height parameter;    -   (v) means for removing said noise segments including means for        substituting a straight line connecting first and third time        instants when the time derivative of said signal is        substantially equal to zero and reassigning segments and their        parameters after the substitution;    -   (vi) means for sorting the remaining segments into a plurality        of wave bands based on the value of their width parameter, each        wave band being defined by upper and lower frequencies        corresponding to lower and upper values for the width parameter        respectively; and    -   (vii) means for classifying a time interval of the signal data        as belonging to one of predefined sleep states based on relative        frequency of occurrence of said segments in said wave bands.

The so-called “segments” are the principal building blocks of EEG andEOG analysis. A “segment” includes a sequence of consecutivelyincreasing and decreasing or consecutively decreasing and increasingintervals of the signal under analysis.

All “segments” may be initially detected by applying syntactic analysisto the signal, ie. detecting all local maxima and minima. As a datastructure a “segment” is represented by its orientation (ie. “upward” or“downward”), width, height and error. In the context of visual signalinterpretation, the last three parameters have a clear meaning. Widthrelates to the dominant frequency of the signal under analysis at thisparticular time interval, height relates to the magnitude of the signalvariation and error, which is a measure of signal variation from astraight line connecting the start and end of the “segment”, relates tothe magnitude of noise in the signal if the “segment” is a part of thenoise rather than a part of the actual signal that is under analysis.

After all “segments” are originally detected using a syntacticalgorithm, those segments which are likely to be noise rather than thesignal under analysis must be removed, and new signal “segments” must bereconstructed. To achieve this an iterative procedure of identifyingnoise “segments” and generating new signal “segments” may be employed. A“segment” may be classified as noise if its width is relatively small(which in the case of EEG signal indicates alpha, sigma and betabands—where high frequency noise is typically prominent) and the erroris relatively small (which ensures that genuine visible EEG highfrequency components are retained). Various rules may be generated torepresent meaningful conditions of small width and small error. This“segment” may then be approximated as a straight line and a new“segment” constructed as a result of this approximation. This proceduremay be performed iteratively until no noise “segments” are detected. Thedescribed approach has a significant advantage over prior art FFTmethods (which cannot discriminate between high-frequency noise andsharp slopes of genuine EEG patterns) and zero-crossing methods (whichrely on DC offset and do not remove noise).

All remaining “segments” may then be sorted according to the value oftheir width parameter among conventional EEG frequency bands. Thissorting may be performed for both “downward” and “upward” “segments” toenable accurate interpretation of asymmetrical “segments”. Once the“segments” are sorted for an interval equal to one sleep study epoch, asimplified sleep/wake discrimination may be performed by calculating atotal duration of “sleep-like” “segments” (sum of durations of all deltaand theta “segments”) and comparing it with the half epoch duration.This approach in fact represents a mathematical model of sleep/wakediscrimination based on visual interpretation of an EEG epoch.

Various means for fine-tuning this technique to achieve more accuratedetection of important EEG patterns and subsequently more accuratesleep/wake discrimination are disclosed below. These include algorithmsfor EEG artifact detection, delta wave detection, periodic patterndetection and modified sleep/wake discrimination rules which take intoaccount a major role of EEG periodic patterns (which may vary beyondalpha band), role of context based decisions and the uncertaintyassociated with artifacts.

The apparatus may include means for detecting and processing artefactpatterns in said signal including one or more of:

-   -   means for detecting flat intervals in the signal;    -   means for detecting intervals in the signal having a relatively        sharp slope, being intervals in which variation in the signal        exceeds a first threshold over a time interval equal to or        shorter than a second threshold;    -   means for detecting intervals in the signal having a relatively        narrow peak, being intervals in which the width parameter is        equal to or less than a third threshold and the height parameter        is equal to or greater than a fourth threshold; and    -   means for detecting other non-physiological pattern in the        signal, being combinations of segments having a width and height        of one, the segments in the combination being less than the        respective total duration and signal variation of the        combination by at least preset ratios.

The apparatus may include means for detecting and processing wavepatterns characterised by minimum amplitude and minimum and maximumdurations, including:

-   -   means for detecting a core interval of the wave pattern as a        sequence of one or more segments which starts at a first time        instant of a first segment when a time derivative of the signal        is substantially equal to zero and ends at a second time instant        of the last segment when a time derivative of the signal is        substantially equal to zero, or starts at the second time        instant of the first segment when the time derivative of the        signal is substantially equal to zero and ends at a third time        instant of the last segment when the time derivative of the        signal is substantially equal to zero, with the total signal        variation of at least the minimum amplitude, duration of at        least a preset share of the minimum duration, less than the        maximum duration and the maximum deviation from a monotonous        change of at least a preset share of the total variation.

The apparatus may include means for detecting a start and end of a mainwave of the wave pattern by subsequent comparison with a presetthreshold of a deviation of the slope of respective components ofsegments preceding and following the core interval from the slope of thecore interval, and for updating the core interval if the deviation ofthe slope and maximum deviation from the monotonous change do not exceedrespective preset thresholds, and a total updated duration is equal toat least a preset share of the minimum duration and is less than themaximum duration.

The apparatus may include means for detecting one or two side waves ofthe wave pattern by subsequent testing of sequences of combinations ofsegments preceding and following the main wave for the signal durationconditions.

The means for sorting into a plurality of wave bands may be based on thedetected wave patterns. The means for classifying may include means forcomparing to preset threshold values of weighted combinations ofoccurrences of the segments in the waveband, artefact patterns and wavepatterns. The apparatus may include means for detecting periodicpatterns with specified minimum and maximum frequencies, minimumamplitude and minimum number of waves including:

-   -   means for selecting combinations of a specified number of        segments;    -   means for assigning for each combination, an average, minimum        and maximum amplitude and an average, minimum and maximum        period;    -   means for testing if the average amplitude exceeds a specified        minimum amplitude for a periodic pattern;    -   means for testing if the maximum amplitude exceeds the minimum        amplitude by not more than a specified ratio;    -   means for testing if the frequency corresponding to the average        period is equal to or greater than the minimum frequency of the        periodic pattern and is equal to or less than the maximum        frequency of the periodic pattern;    -   means for testing if the maximum period for a combination of        segments exceeds the minimum period by not more than a specified        ratio;    -   means for joining combinations of segments, which comply with        the above criteria; and    -   means for classifying a time interval of the signal data as        belonging to one of predefined states on the basis of a        comparison of the value of a weighted combination of durations        of a plurality of wave bands, artefact patterns and wave        patterns with a threshold which is set to a different value        depending on the total relative duration of periodic patterns        within the time interval.

The apparatus may include means for classifying a time interval of thesignal data as belonging to one of predefined states on the basis of acomparison of the value of a weighted combination of durations of aplurality of wave bands, artefact patterns and wave patterns with adecision boundary which is set to a different value depending on thetotal relative duration of periodic patterns within the time interval,if the difference between the value and the decision boundary is equalto or greater than a specified margin, or otherwise, on the basis of acomparison of this value with the respective value for the preceding orfollowing time interval providing that that interval is alreadyclassified and the difference between the respective values is equal orless than the specified margin, or otherwise, if after subsequent passesthrough the data, an interval is still not resolved, on the basis ofcomparison of this value with a threshold which is set to a differentvalue depending on the total relative duration of periodic patternswithin the time interval.

According to a still further aspect of the present invention there isprovided a sensor for detecting position of an eye lid including:

-   -   first means adapted to move substantially with said eye lid and        relative to a second means; and    -   means for providing an electrical signal indicative of the        position of said first means relative to said second means, such        that said signal includes a measure of position and/or degree of        opening of said eyelid.

The first and second means may be electrically coupled such that thecoupling provides the measure of position and/or degree of opening ofthe eyelid. The first and second means may be provided by respectivearms connected for relative movement. The arms may be pivot ablyconnected to each other. Each arm may include a capacitive elementarranged such that the extent of overlap between the arms determines thecoupling between the capacitive elements. Each capacitive element mayinclude one plate of a capacitor. Alternatively each arm may include aninductive element arranged such that the extent of overlap between thearms determines the coupling between the inductive elements. Eachinductive element may include a coil. The sensor may include means suchas a wien bridge for measuring the capacitive/inductive coupling betweenthe capacitive/inductive elements.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments of the invention will now be illustrated anddescribed with reference to the accompanying drawings wherein:

FIG. 1 shows an overview flow diagram of one form of ADMS according tothe present invention;

FIG. 2 shows a graphical representation of typical AEPi and Bi functionsversus time for a patient undergoing general anaesthesia;

FIG. 3 shows a flow chart of one form of bicoherence, real tripleproduct and bispectral index analysis;

FIG. 4 shows one form of sleep staging analysis;

FIG. 5 shows a block diagram of sample AEP:BIC analysis (Mode 1)associated with weighting arbitration;

FIG. 6 shows a flow diagram of sample AEP:BIC analysis (Mode 1)associated with weighting arbitration;

FIG. 7 shows a simplified overview of scaling factor and transitioncurve functions associated with the ADMS;

FIG. 8 shows a graphical representation of CIAi, TCU & TUC values;

FIG. 9 shows a graphical representation of absolute value of Ai+Bi, TCU& TUC;

FIG. 10 shows a graphical representation of AEPi;

FIG. 11 shows a graphical representation of Bi;

FIG. 12 shows a graphical representation of Ai;

FIG. 13 shows a graphical representation of BMi;

FIG. 14 shows a graphical representation of Bi with the colour of thebackground changing to indicate transition of consciousness state;

FIG. 15 shows a flow chart of an improved system for monitoringconsciousness according to a preferred embodiment of the presentinvention;

FIG. 16 shows a simplified functional system overview (FSO) of apreferred embodiment of apparatus according to the present invention;

FIG. 17 shows a more detailed functional system overview (MDFSO) of apreferred embodiment of apparatus according to the present invention;

FIG. 18 shows a main flow diagram (MFD) of the HCM system according to apreferred embodiment of the present invention;

FIG. 19 shows a flow diagram of one form of EEG analysis formatvalidation in Block 8 of FIG. 18;

FIG. 20A shows a flow diagram of computation of bicoherence, real tripleproduct and bispectral index in Block10 of FIG. 18;

FIG. 20B shows a graphical representation of bispectrum, bicoherence andreal triple product in Block 10 of FIG. 18;

FIG. 21A shows a sample signal applied to a patient's ear(s);

FIG. 21B shows a signal similar to FIG. 21A at a lower sensitivity;

FIG. 21C shows a block diagram of hardware for generating the signals inFIGS. 21A and 21B;

FIG. 21D shows one form of hardware for collecting AEP sensory data froma subject;

FIG. 21E shows an example of the signal from the subject's ear sensorynerve when receiving signals as shown in FIGS. 21A and 21B;

FIGS. 21F and 21G show examples of AEP output graphs for a range ofinput frequency sweeps;

FIG. 21H shows a sample of response curves from AEP input electrodes;

FIG. 22A shows a bar graph of Context Analysis Method and FIG. 22 ashows the corresponding display validation status;

FIG. 22B shows a bar graph of Context Analysis Probability and FIG. 22 bshows the corresponding display validation status;

FIG. 22C shows a bar graph of Transition Analysis Method and FIG. 22 cshows the corresponding display validation status;

FIG. 22D shows a bar graph of Transition Analysis Probability and FIG.22 d shows the corresponding display validation status;

FIG. 22E shows a bar graph of Movement Analysis Method and FIG. 22 eshows the corresponding display validation status;

FIG. 22F shows a bar graph of Movement Analysis Probability and FIG. 22f shows the corresponding display validation status;

FIGS. 23A to 23C show graphical representations of system output alarms,indicators and displays associated with Block 15 of FIG. 18;

FIG. 24 shows a flow diagram of arousal detection in Block 16 of FIG.18;

FIG. 25 shows a flow diagram of the process of detecting zero derivativetime instants and elementary maximum segments in Block 21 of FIG. 18;

FIG. 26 shows a flow diagram of the process of detecting zero derivativetime instants and elementary minimum segments in Block 21 of FIG. 18;

FIG. 27 shows a flow diagram of the process of sleep/wake analysis andBIC EEG artefact removal in Block 21 of FIG. 18;

FIG. 28 shows weighted and display normalized BIC and AEP data;

FIG. 29 is a sample of combined and weighted BIC and AEP data withcritical threshold and patient state display;

FIGS. 30A and 30B are tables showing examples of weighting for combined(1, 2, 3, 4, 5) analysis index in Block 35 of FIG. 18;

FIG. 31 shows an example format for transition weighting based uponcontext analysis in Block 37 of FIG. 18;

FIG. 32 shows a flow diagram for determiningconsciousness/unconsciousness using combined AEP and BIC index and R & Kin decision context in Block 37 of FIG. 18;

FIG. 33 shows one form of apparatus for wireless linked continuous bloodpressure measurement;

FIG. 34A shows one form of sensor device for sensing and measuring eyeopening;

FIGS. 34B and 34C show alternative forms of the electronic interfaceshown in FIG. 34A;

FIG. 35 shows one form of electrode system for integrated anaesthesiamonitoring;

FIG. 36 shows one embodiment of a wire connected sensor device includingbi-coherence, EOG, chin EMG and eye opening;

FIG. 37 shows one embodiment of a wireless integrated electrode systemincluding bi-coherence, EOG chin EMG and eye opening;

FIG. 38 shows a preferred embodiment of a wireless electrode;

FIG. 39 shows a flow chart of master firmware;

FIG. 40 shows a flow chart of slave firmware;

FIG. 41 shows an overview of primary, secondary and tertiary analysis;

FIG. 42 shows one form of vehicle bicoherence wireless system;

FIG. 43 shows a flow diagram of one form of audio and video apparatusused for validating and replay in an in-depth anaesthesia system;

FIG. 44 shows one form of pain level or consciousness level remoteindicator;

FIG. 45 shows a spread spectrum based wireless, active electrode system;

FIG. 46 shows an indirect connected wireless module;

FIG. 47 shows one embodiment of a wireless based active electrodesystem;

FIG. 48 shows a drug delivery system linked to a consciousnessmonitoring device; and

FIG. 49 shows a power spectral curve of sample data.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

One form of Anaesthesia Depth Monitoring System (ADMS), which utilizes acombination of Bispectral index (Bi), Audio Evoked Potential index(AEPi) and Sleep Staging Analysis (SSA) for improved Depth ofAnaesthesia monitoring will now be described with reference to FIGS. 1to 14. The ADMS may present to the user a single Index of 1 to 100,where 100 represents the highest state of a monitored patient'sconsciousness and 0 represents the lowest index of a monitored patient'sstate of consciousness.

AEPi, in the context of prior art depth of anesthesia monitoringsystems, has been reported as being more sensitive (than Bi alone, forexample) in the detection of the transition from unconsciousness toconsciousness. AEPi has also been reported to be more responsive than Bito patient movement in response stimuli. However, Bi has been reportedto increase gradually during emergence from anesthesia and therefore maybe able to predict recovery of consciousness at the end of anesthesia(Gajraj et. al. 1999).

Prior art depth of anesthesia monitoring systems typically deploy eitherAEPi, Bi or both indexes as separate measures. The monitoring of AEPiand Bi as a combined index (CIAi) is preferable in terms of acomprehensive depth of anesthesia monitoring system with the ability todetect transition from unconsciousness to consciousness (TUC), patientmovement (AEPi) and the benefits of a measure for the gradual emergencefrom anesthesia (utilizing Bi).

A depth of anesthesia monitoring system should be simple and unambiguousin its' use. This presents a problem because while a single Bi or AEPindex presents a simple user-friendly system, the scope of a singlemeasure (Bi or AEP) limits the accuracy of measurement of depth ofanesthesia. On the other hand, relying on two separate measures (ie.AEPi and Bi) can complicate system operation by producing confusion,such as an ambiguity as to which of the two measures should be followedat any one point in time.

One problem with conventional depth of anesthesia systems that aredependent only upon Bi, for example, is an inability to detect atransitional change from unconsciousness to consciousness.

As can be seen in FIG. 2, the gradual change in the Bicoherence in ZoneC presents the anesthetist with a more gradual indication of thetransition from unconsciousness to consciousness. In contrast to the Bithe AEPi does not present a gradual change during emergence fromunconsciousness but does provide a more clear depiction of thetransition point from unconsciousness to consciousness. Concerns mayexist in that the gradual changes in Bi may not distinguish clearly orquickly enough sudden transition changes, such as would be required todetect instances of a subject “prematurely” emerging fromunconsciousness. These “instances” of early awakening (from anesthesia)can lead to potentially traumatic occurrences of memory recall and otherassociated effects. If, for example the TCU and TUC phases are notimmediately apparent then the chances of instances such as audio recallwill increase. Furthermore, the nature of the Bi EEG data is less likelyto distinguish the audio sensory nerve shutdown and awakening aseffectively as AEPi, due to the more direct hearing sensory analysisassociated with AEPi analysis. Thus claims of audio recall with Bi basedsystems may render these technologies under greater scrutiny in futuredepth of anesthesia monitoring applications.

The ADMS addresses these disadvantages by integrating the strength ofboth AEPi and Bi, while still providing a single user-friendlyComprehensive Integrated Anesthesia index (CIAO (refer STEP 23 and STEP24).

A further difficulty exists with the current state of the art depth ofanesthesia monitoring in that variations between different patients canalter the depth of anesthesia monitoring parameters with each individualpatient. Examples of variations that can change the monitored parametersbetween different patients include different levels of hearingperformance between the different patients. This variation can be ofparticular importance where the comparison of AEPi transition thresholdsand critical thresholds, for example, is of importance. Other examplesof variations between patients, which can affect depth of anesthesiamonitoring outcomes between different patients, include gender, bodymass different sleep architectures, amongst other factors.

The ADMS of the present invention may alleviate these difficulties byincorporating an automatic patient calibration function. (refer STEP 5and STEP 6).

A further difficulty exists where a depth of anesthesia system requiresease of use but at the same time may be required to accommodate theflexibility associated with providing a range of system configurations.This range of different system configurations can exist due to the factthat some Depth Of Anesthesia (DOA) applications may not be practical,for example, to attach multiple sensor or electrode systems (such asboth AEP and B sensors and electrodes). These situations may occur wherethe simplest electrode or sensor configuration is required. Furtherexamples of where the system configuration may need to change includesituations where various electrodes or sensors may not be performingreliably or to a minimal standard suitable for the monitoring andsubsequent analysis or various parameters.

In situations where the user elects to use one or more differentparameters or where electrodes or sensors are detected to not beperforming appropriately, it would be desirous for the DOA monitoringsystem to allow the user to select or change the sensors or electrodestatus, or alternatively for the DOA system to notify the user andautomatically compensate for the current electrode and sensorconfiguration or performance.

The ADMS may address these difficulties by incorporating automaticsensor and electrode scan (STEP 2) and MODE configuration (STEP 4).

A further difficulty which arises with the task of integrating AEPi andBi is that in order to determine which of the AEPi or Bi is mostappropriate at any given point in time, an independent method ofarbitrating may be required to determine which of the two methods (AEPior Bi) should be utilized or given higher weighting at any particularpoint in time.

The ADMS may address these difficulties by incorporating Sleep StagingAnalysis (SSA) as a means of independent arbitrating for weighting ofAEPi versus Bi. SSA analysis may provide a determination of the contextof a patient's depth of anesthesia monitoring state and the state of asubject's consciousness based upon SSA spectral based analysis, incontrast to Bi analysis basis of phase difference and AEPi basis ofaveraging BSAEP amplitude signals synchronized to the patient's hearingresponse. Where “context analysis” refers to whether the patient's isentering a state of consciousness or emerging from unconsciousness(refer STEP 16 and STEP 24).

The ADMS of the present invention may integrate into a simple andsingular index the benefits of both AEPi and Bi for the optimalmonitoring of a patient during anesthesia. The new system uses anindependent (to AEP or Bi) method of analysis being spectral based or ½period amplitude analysis (34, 35, 45, 46) as a means to arbitrate whichof the Bi and AEPi methods are most optimal at any point in time.Furthermore, ADMS can provide weighting to these said methods in orderto combine or integrate the monitoring of a subjects depth of anesthesiainto a simple but accurate single index.

Other difficulties with prior art technologies include an inability torespond to a subject who may be paralyzed due to muscle sedation andparalysis drugs administered in the course of an operation to preventunwanted body movements during the operation. There have been incidentsreported where patients were indeed conscious or partially consciousduring an operation and due to the influence of paralyzing drugs wereunable to alert medical staff.

ADMS may alleviate these difficulties with the use of Arousal index,Body Movement index and associated sensors, allowing the patientmovement status and/or arousal status alert, independent of CIAi (STEP23). A further difficulty that exists with the current state of the artdepth of anesthesia monitoring systems is the inability to calibrate thesensitivity of the monitoring device with each patient's individualvariations and sensitivity to anesthesia.

ADMS may alleviate this difficulty by incorporating a means of usingCalibrated Patient (CALPAT) values to modify or adjust these saidtransition threshold values on an individual patient by patient basis(refer STEP 5 and STEP 6). The means of this Automatic Calibrationmethod are based around measuring the patient's AEPi, Bi and SSA duringthe patient's first occurrence of transitioning from Consciousness toUnconsciousness. Thereafter Calibration of Patient transition values anddisplay zone values can be allocated specific to the individualpatient's sensitivity to AEPi and Bi.

A further difficulty with prior art AEPi based depth of anesthesiamonitoring systems is the difficulty to compensate or accommodate forthe varying hearing performance (or response to audible stimuli) betweendifferent patients and also between different audio stimulus apparatusand attachment of the audio stimulus apparatus thereof(Lippincott-Raven, 1997). These variations can be caused by factors suchas the means used to generate the audio stimulus, the attachment methodand device type (typically a single or pair of ear pieces are used togenerate audio click stimulus in ear piece), or physiological variationsin hearing performance evident between different subjects. The ADMS ofthe present invention may adjust the frequency spectrum, amplitude andphase of the audio click in order to provide optimal compensation fordifferent audio stimulus device and attachment types and hearingvariations (113).

It has been reported in recent publications (Vuyk 2002) that prior artdepth of anaesthesia monitoring systems suffer several concerns. Oneconcern relates to the use of the bispectral index monitor (AspectMedical Systems, Inc., Natick, Mass.). This device relies upon thebispectral index (BIS) to monitor consciousness-sedation-unconsciousnesslevels. However it has been reported that various anaesthetic agents onthe bispectral index scale appear to be agent specific. In general, ithas been reported that agents such as propofol, midazolam or thiopentalhave a strong depressant effect on BIS. It has also been reported thatinhalation anaesthetic agents propagate an intermediate depressanteffect on BIS. However, it has been reported that the opoids have littleor no influence on the BIS at clinically relevant concentrations. Alsodisconcerting is the fact that nitrous oxide and ketamine appear to haveparadoxical effects on the BIS. Accordingly it has been suggested thatBIS may relate well to sedation and hypnosis levels but does notproperly reflect level of analgesia or depth of anaesthesia.

One inherent difficulty with the prior art depth of anaesthesiamonitoring systems relying upon BIS (Aspect) as the main index ormeasure of depth of consciousness is the risk that a patient may lapseinto a state of consciousness or indeed not enter or continue theirstate of unconsciousness, during critical times of an operationprocedure.

The ADMS system of the present invention may alleviate or reduce risk ofthis difficulty by incorporating a Brain Stem Audio Evoked PotentialMiddle Latency (BSAEPML) signal as a precise indicator of a patient'stransition from consciousness to unconsciousness and transition fromunconsciousness to consciousness. This added factor of monitoring mayoperate in real-time and simultaneously with the measure of bispectralanalysis. Thus different effects that can relate to differentanaesthetic agents are reduced or alleviated by a measure of BSAEPMLwhich is directly related to thermus and temporal lobe generators whichin turn are directly related to the patients state of consciousness, andmost importantly by definition of the function of (BSAEPML), to risk ofmemory recall associated with critical times within operationprocedures.

The prior art utilises BSAEP, typically being ½ second frequency audiostimulus clicks as a method of auditory sensory stimulation. The ADMSmay incorporate a steady state Audio Evoked potential capacity,including a capacity to provide higher continuous frequency of audioclicks. The range of audio frequency capacity may be from ½ Hz to 100Hz. The steady state BSAEP may provide greater sensitivity in that moresubtle changes may be measured from the subject's BSAEP. Responsivenessmay also be more precise due to the fact that there is less time betweenconsecutive stimuli clicks and therefore there is less delay and lesslikelihood in missing physiological responses.

The ADMS may include a capacity to provide interactive SSAEP. The lattermay provide click stimulus sequences with a different or varying rateaccording to the patient state detected. This dynamic or programmableclick stimuli may allow the system to “validate” more accurately andprecisely patient status under various stimuli frequency and amplitudeconditions, where these conditions can vary the stimuli test sequence.

The ADMS may include a capacity to provide multiple frequency (typicallydual frequency) steady state BSAEP. In particular 40 Hz and 80 Hzcontinuous click stimuli frequencies may be used, where the 40 Hz and 80Hz click rates are sequentially toggled or switched between. A typicalsequence may be 5 seconds of each continuous 40 Hz audio click stimulifollowed by 5 seconds of continuous 80 Hz audio click stimuli, in acontinuous sequence toggling between 40 Hz and 80 Hz. The 40 Hz (orsimilar lower frequency rate) audio stimuli allows middle latencytesting of the AEP, which may provide a graduated measure or monitoringin accordance with the subject's state of consciousness but moreimportantly, a precise transition state from consciousness tounconsciousness and unconsciousness to consciousness. The 80 Hz audiostimuli and corresponding AEP may allow a graduated measure andmonitoring method for brain stem cortical response which is an importantsignal for detecting a patient's risk of neurological damage or risk ofserious or fatal over sedation of anaesthesia, for example. This 80 Hz(or similar higher frequency rate) may provide an ideal alert or warningmeasure for the ADAMS to prevent or reduce risk of over-sedation orexcessive depth of anaesthesia. This 80 Hz Continuous State and is BSAEPsignal is a key measure for brain life or death status.

The ADMS integrated index and integrated monitoring capability mayvastly improve upon the prior art by incorporating an important measureof hypnotic consciousness state (utilising bispectral analysis),incorporating effective transient state measure from consciousness tounconsciousness and unconsciousness to consciousness utilising lowercontinuous frequency such as 40 Hz click stimuli, and also incorporatingcritical brain stem cortical status warning and alert capability,particularly for reduced risk of over administration of anaesthetic orsedation drugs.

Multiple frequency (typically dual frequency) Brain Stem Stead StateAudio Evoked Potential monitoring and subsequent electrode attachment,and bispectral monitoring and subsequent electrode attachment may beachieved with as few as one and as simple as one self-adhesive electrodeattachment strip, requiring little or no electrode preparation. This isdue to the ADMS system's capability to deploy the above mentionedwireless and/or battery powered audio stimulus earpiece and also asingle electrode substrate with a unique combination of 2 inbuiltforehead EEG electrodes (for bispectral EEG signal-attached nearpatient's forehead outer malbar bones) and one further inbuilt electrodewhich may be positioned about 1 to 2 cm from the patients centre eartowards the patient's nose.

This single self-adhesive substrate with as few as three inbuiltelectrodes may optionally include a disposable electrode format. Afurther option may allow a self sealed outer sterile cover typicallyplastic or foil to be removed to access the new electrode. The sensormay optionally contain an in-built electrode and a use by date of theelectrode may be clearly marked on the outer electrode packaging. Thissystem of inbuilt battery and due date labelling may avoid conventionaltechnology issues and risk of both cross infection and flat batteriesduring critical use. A light, durable and non-obtrusive electronicsmodule may optionally be clipped to the electrode substrate providingwireless interface to the ADMS measuring device.

The ADMS may include active electrodes, whereupon an electronics module(separate or inbuilt and disposable) may provide close location ofamplifier buffering to the inbuilt electrodes. This closely locatedelectronic buffering circuitry may provide an electrode system, which isless vulnerable to stray capacitance and external noise pick-up.Electrode impedances may be higher and may avoid issues of prior artelectrode systems, whereupon extra preparation is required to clean andabrade the patient skin surface, to achieve acceptably low impedancebetween patient connections. Typical impedances with non-activeelectrodes may be 5 kilo-ohms compared to 50 Kilo-ohms or more, which isacceptable with active electrode configurations.

The electrode buffer (and/or amplification and filtering) electronicsmay be embedded within the substrate of the electrodes directly near thein-built electrodes, using flexible printed circuit techniques, such ascircuit tracks printed on or within the electrode substrate.

A further feature of the ADMS electrode connection is that it may notrequire direct electrical connection to the patient. A new approach tohuman electrical activity detection has been made possible by recentadvances in ultra-low-noise, ultra-high-input impedance probes. Theseprobes do not require a real current conducting path and operate on thegeneral principle of induction of a signal from a non-contact source.

This technology may provide a unique application for the ADMS electrodeconfiguration options. Electrophysiological connection of the foreheadEEG connections allowing monitoring and analysis for AEP and bispectralanalysis may be implemented by electrical probes, which are embeddedinto the electrode substrate device and thus allow signal monitoringwith minimum invasion.

The electrode and wireless systems may be used in a configuration ofonly forehead electrode provision for predominantly bispectral analysis.This type of simplified configuration may be especially suited todriving, operator or other vigilance monitoring and may be life savingfor detection of fatigue onset (change in hypnotic and consciousnessstates can be detected, for example). The vehicle driver using thissystem may simply opt to open the disposable electrode packet, remove abattery start enable paper tag and self-attach the discrete andvirtually undetectable wireless electrode to the forehead under thehairline, while a wireless mounted dash (or cigarette lighter connected)device monitors and alerts the fatigued or drowsy driver, potentiallypreventing a fatal road accident.

A further concern of prior art depth of anaesthesia monitoring systemswhich do contain some form of (BSAEPML) is that the attachment of anearpiece or other means of auditory stimulus systems to a patient duringa surgical operation or other medical procedure can be disconcerting,too invasive and wires and cable can indeed cause unnecessary orpotentially distracting concerns of entanglement or other adverseeffects.

The ADMS system may address these difficulties and limitations byutilising a real-time wireless connected audio stimulus device, which isdesigned to avoid reliance on wires, may be as small as the tiniesthearing aid and may be attached to the patient with a simplenon-invasive insertion process. Furthermore the device may include adisposable cover system designed to avoid cross infection while allowingthe more expensive audio stimulus device to be re-used.

Furthermore the speedy disposable changeover cover system may include aunique protective-disposable-cover option providing high reliability andconvenience of a wireless BSAEPML audio stimulator whilst being highlyuser friendly and attractive for critical environments such as operatingtheatres. This provides a protective cover with an integrated disposablebattery and an industrial design which allows a totally fool proofattachment of the cover with a “snap in” battery function (eitherrechargeable or single use). The “snap in” function, denotes that whenthe user opens a sealed pack which displays the use by date for thecover-battery (protective cover for audio stimulus device withintegrated battery), the user has a simple battery activation means suchas removal of a paper or cardboard tag labelled for example “remove whenready to start. This type of methodology may ensure that the user neverneeds to contend with flat battery issues while the protective coverwith integrated battery avoids cross infection. The “fool-proof” batterywith cover method at attachment and use may be by way of the batterysliding, clipping, magnetic slotting, or slotting only into or onto orpart of the wireless audio stimulus device.

The method of incorporating an anti-cross-infection and batterymanagement system into a foolproof cover system alleviates two majorissues namely, risk of the battery going flat and cross-infection andmay be adopted in all sensors and electrode applications.

FIG. 1 shows an overview flow diagram of an ADMS according to apreferred embodiment of the present invention. Steps 1 to 26 in the flowdiagram are described below.

Step 1—Start Up ADMS and Attach Patient Electrodes and Sensors

For ease of use and minimal electrode configuration a typical electrodeattachment system may include a single self-adhesive forehead electrodesystem. Alternatively a single sensor device extending over thepatient's forehead and chin may be used, to allow the forehead EOG, EEGand reference connections/AEPi connections (where AEPi reference mayinclude a mastoid connection), while also allowing EMG via chin surfaceand mastoid reference near patients ear and optionally wire or wirelessconnected earpiece for audio stimulus connection. The electrode devicecould contain 3 electrodes whereupon Bi and SSA EEG signals are derivedfrom the forehead electrode connections (outer malbar), EOG signals arealso estimated from the forehead connections and reference is derivedfrom the central forehead connection. Alternatively the electrode devicecould contain 6 electrodes, being the abovementioned electrode with theaddition of 2 electrodes for detection of chin EMG (for SSA EMG andarousal detection), mastoid electrode connection for bipolar referencesignal where the aforementioned forehead connection provides the BSAEPisignal, and optional earpiece interface or connection for AEP audiostimulus. SSA EEG and EOG signals would be derived as estimations ofconventionally placed (per sleep monitoring clinical standards (34,114)) for EEG, EOG and EMG signal monitoring. Estimations are requiredin order to allow the minimal and simplified ADMS configuration, whilestill providing SSA.

Incorporating the self-adhesive forehead attached electrode system as asingle attached substrate would provide simplification and ease of use.A wire or wireless connected ear-phone can be applied to one or both ofthe patient's ears for the purpose of generating the AEPi stimulus clicksound. Where wireless configuration is used the earpiece could beconnected to the electronics module for power and wireless controlinterface. One of the unique aspects of the ADMS system is the abilityto adjust the volume of the stimulus click beyond a default “normal” (orstandard value per empirical data (ref 1)), in order to compensate forhearing performance variations between different patients.

The electronics module can clip or attach to the outer surface of thedisposable electrode substrate and provide wireless interconnection andactive electrode functions. Embedded within the disposable electrodesubstrate could optionally be a disposable battery, thus avoiding theneed otherwise for recharging of electronics module battery source(electrode may be subject of separate patent). Optionally the electrodesystem can be designed as a re-usable device.

The Electronics Electrode and Sensor Module (EESM) can be a fast chargesystem with charge capability electrically connected (for example, viaelectrode press-stud connections), slow charge system (via induction orRF interface to EESM recharge circuits), or a combination of bothsystems. Clear EESM indication (ie. LEDs status) of remaining batterylife and remote warning of pending battery flat alert is always active.An easy to clean container neatly holds EESM module and sensors, whileat the same time providing ongoing charge function. One or more LEDSprovide a clear status at any time for the remaining hours of chargeenergy.

Psuedo Code Sample (Psuedo code may be expanded upon or deleteddependent upon whether the preferred embodiment demands such detail);ADMS Initialization;System initializes with STARTADMS in deactivated (switch-up) position;ADMS=0System initializes in the uncalibrated patient mode; CALPAT=O(calibrated uninitialised).STARTADMS=0 when start switch is de-activated (up position).STARTADMS=1 when start switch is activated (down position).

Step 2. Automatic Sensor and Electrode Check and Status

Connected electrodes are detected for purpose of automatic systemconfiguration and notification of automatic sensor and electrode qualitystatus check.

Periodic impedance scanning of all electrophysiological electrodesprovides the ADMS the capability to detect a deterioration of signalquality at any point in time. The operator is then provided the optionto correct the poor electrode connection. Alternatively the ADMS isautomatically re-configured to accommodate a revised configuration,designed to monitor a subject's depth of anesthesia in the absence ofthe disconnected or poor electrode contact(s).

Step 3. AEP Automatic Calibration

One of the impediments of previous art systems using BSAEPi as a markerfor monitoring depth of anesthesia is the difficulty to compensate oraccommodate for the variatations in hearing performance (or response toaudible stimuli) between different patients (Lippincott-Raven, 1997).

These variations can be caused by factors such as the generation of theaudio stimulus attachment method and device type (typically a single orpair of ear pieces are used to generate audio click stimulus in earpiece), or the physiological variations in hearing performance evidentbetween different subjects. The ADMS is capable of adjusting thefrequency spectrum, amplitude and phase of the audio click in order toprovide the optimal compensation for variations between different audiostimulus devices, variations due to different attachment methods (ofaudio stimulus device ie. ear piece or headphones), or variations due todifferent hearing performance between individual patients (112).

An object of the present invention is to provide a means to calibratethe ADMS AEPi monitoring function for each specific patient's hearingresponse. This capability, within the ADSM, is achieved by providing arange of audio calibration stimuli signals and measuring the AEPiresponse to this said range of stimuli. While measuring the AEPiresponse the data is compared to empirical clinical data. The SoundPressure Level (SPL) of the audio stimulus can be adjusted until thedesired response, as comparable to the normal standard hearing patients,as referenced from the empirical data.

Factors such as polarity of the stimulus delivery apparatus can bechecked and compensated for, as required. At all times safe SPL levelscan be verified to ensure safe audio stimulus conditions.

One of the unique aspects of the ADMS system is the ability to adjustthe volume of the stimulus click beyond the default “normal” value, inorder to compensate for hearing performance variations between differentpatients.

In the circumstances, where patient's BSAEP signal does not respond tothe AEP threshold levels as expected from “normal” patient hearingperformance, the ADMS system provides a servo gain control function. Theservo system is achieved by adjusting the AEP audio stimulus clickamplitude level (while ensuring safe levels are not exceeded at anytime) until the AEPi signal derived from the patient's data is similarto the levels as expected from normal patients. Further calibration ofthe AEPi signal can be achieved by detecting a particular patientshearing performance at different frequencies or combinations offrequencies and different levels thereof, and optimization of spectralcontent of the audio stimulus signal in order to compensate for eachpatient's individual hearing variations. In this manner the mostreliable or efficient hearing performance conditions can be determinedfor each particular patient to ensure the AEPindex is derived for thatpatient under the most stable and reliable AEP stimulus frequency andamplitude conditions on an individual patient by patient basis. Thesesame principals can be used for the automatic and optionally remoteservo control of hearing aids (a subject of separate patent).

Step 4. Automatic ADMS Configuration

* The ADMS system is capable of providing user adjustable or factorydefault MODES. A library of MODE configurations can be configured fordifferent patient types or user specific requirements.

Subject to connected electrodes and sensors and the status of each ofthe said sensors and electrodes (per above step) the ADMS systemdetermines the system configuration.

MODE 1—Integrated AEPi and Bi.

MODE 2—Bi only.MODE 3—AEPi only.Option 1—Body Movement Multi-zone movement Biomat sensor.Option 2—Body Movement Single-zone movement Biomat sensor.Option 3—Electrophysiological Arousal Detection (derived AEP and/or BEEG electrodes).

ADMS MODES 1 to 6 can be configured (automatically or with manualassistance) with options 1, 2 or 3. The ADMS system will detect thepresence of a mattress sensor and type as being single or multi-zone.The ADMS system will also, by default, detect the forehead EEGelectrodes and the chin EMG electrodes for arousal analysis and eventdetection. A logical OR function will, by default, display an arousalevent if an arousal is detected from the forehead EEG OR the Chin EMGelectrodes. For the purpose of this description of preferred embodimentwe will assume:

MODE 4—Integrated AEPi and Bi with SSA as Bi-AEP arbitrator.Option 1—Body Movement Multi-zone movement Biomat sensor.Option 3—Electrophysiological Arousal Detection (derived from chin EMGelectrodes of forehead EEG electrodes).Automatic Electrode and Sensor Pass-Fail detect and Mode select.

-   -   Fail condition for AEPi, Bi or SSAi is signaled when any of the        respective electrodes or signals is poor quality. Poor quality        electrodes (for example) would be signaled if the impedance of        the said electrodes were above the acceptable electrode        impedance thresholds. Typical impedance threshold would be 10        thousand ohms impedance, for example. Above this threshold (10K)        value ADMS would signal the user exactly which electrode is not        performing appropriately, what steps can be taken to alleviate        the problem. Alternatively the user can be prompted to request        the ADMS system to reconfigure the system MODE in order to        ignore the poor electrode connection. The 10K threshold can be        changed to the user's selection.    -   Fail condition for AEPi, Bi or SSAi would signal that the        respective index should be weighted to zero. Therefore zero        weighting of analysis in response to signal failure        automatically changes mode in accordance to above STEP 4 and the        following table.

Mode 1 impedance weighting effects are shown in Table 1 below:

TABLE 1 AEPi Bi SSAi Pass Pass Fail MODE 1- Integrated AEPi and Biwithout SSA arbitration. Pass Pass Pass MODE 1- Integrated AEPi and Biwith SSA arbitration. Fail Pass Fail MODE 2- Bi only. Fail Pass PassMODE 2- Bi only. Pass Fail Fail MODE 3- AEPi only. Pass Fail Pass MODE3- AEPi only. BM-SZ BM-MZ AR Pass Fail Fail Option 1- Body Movement (BM)movement BiomatSingle-Zone (SZ) sensor. Fail Pass Fail Option 2- BodyMovement (BM) movement BiomatMulti-Zone (MZ) sensor. Fail Fail PassOption 3- Electrophysiological Arousal Detection. Pass Fail Pass Option3 and 1- Fail Pass Pass Option 3 and 2- NOTE 1: BM-SS = Body MovementSingle Sensor; BM-MS = Body Movement Multi-Sensor NOTE 2: Subject toconnected electrodes and sensors (per STEP 2 above) and the status ofeach of the said sensors and electrodes the ADMS system determines thesystem configuration.

Step 5—Patient Specific Calibration Transition Values—CALPAT

The Transition thresholds of Consciousness to Unconsciousness (TCU orchange of zone A to B), Transition from the deepest stage ofUnconsciousness to a lesser degree of Unconsciousness (change of Zone Bto C) and Transition from Unconsciousness to Consciousness (TUC or ZoneC to D) in the ADMS system is determined from either default values asderived from empirical clinical data (see below) or from values asderived by way of thresholds determined with Calibration of Patient(CALPAT) function.

Graphic reference of AEP and Bi showing phases of a typical anesthesiamonitoring session are shown in FIG. 2. Tables 2 to 4 below describe theassociated ADMS transition zones.

TABLE 2 1. Start of monitoring 2. Zone A C 3. Transition from Zone A toB TCU 4. Zone B U 5. Transition from Zone B to C TSW 6. Zone C U 7.Transition from Zone C to D TCU 8. Zone D C

FIG. 2 presents a typical AEPi and Bi versus time functions for apatient undergoing general anesthesia. The Horizontal axis representstime progressing left to right from the earliest to latest time. Notethe gradual Bi curve ascension in zone C versus the steeper ascension ofAEPi in Zone C for the Transition of patient from Unconsciousness toConsciousness. The new ADMS system produces an ideal depth of anesthesiamonitoring by incorporating a method to deploy the advantages of bothAEPi and Bi, while presenting a simple single anesthesia Depth ofAnesthesia monitoring index.

TABLE 3 ADMS ZONE ZONE A ZONE B ZONE C ZONE D Transition Zone C TCU UTSW U TUC C Default or Empirical Data IDDZA IDTCU IDDZB IDTSW IDDZCIDTUC IDDZD Calibrated Patient Data CPDZA CPTCU CPDZB CPTSW CPDZC CPTUCCPDZD NOTE: Values exist for Bi and AEPi for each of the transitionpoints and zones A, B, C and D.

TABLE 4 Definition of Zones A, B, C, D Zone Ranges CODE and Events. KeyDescription Zone A C Patient in Consciousness State. TCU Transition fromConsciousness to Unconsciousness Zone B U Patient in unconscious state.Zone C U Patient in unconscious state. TUC Patient Transition fromUnconsciousness to Consciousness Zone D C Patient in ConsciousnessState. BM BMe Presence of Body Movement events (ref 34). Ae Ae Presenceof Arousal events (ref 35)

Patient Calibrated values refers to modifying or adjusting these saidtransition threshold values on an individual patient-by-patient basis.

The means of this Automatic Calibration method are based aroundmeasuring the patient's Bi during the patient's first occurrence oftransitioning from Consciousness to Unconsciousness (in accordance withAEPi TCU empirical data transition threshold level (refer step 7).

After Calibration of Patient, transition threshold values (TCU, TUC) anddisplay zones (C, U) can be allocated specific to the individualpatient's sensitivity to AEPi and Bi.

Default empirical data values of AEPi (ref 3), Bi (ref 3) and SSA arecompared to data of the patient's first Transition from Consciousness toUnconsciousness (TCU) is detected by observing AEPi, Bi and SSAtransitioning through the respective TCU threshold values. This initialor first transition of consciousness state serves as a calibration pointfor the ADMS system to optimize to each individual patient's depth ofanesthesia AEPi, Bi and SSAi monitoring sensitivity.

Once the first consciousness to unconsciousness transition has beentracked and analyzed using the ADMS system's CALPAT function, all othertransitions (A to B, B to C, C to D) and monitoring Display Zones (A, B,C, D) can be optimized or fine-tuned to the individual patient'ssensitivity. The relationship between AEPi and Bi at TCU is unique toeach patient and can be used to extrapolate each individual patient'sTUC threshold values.

The ADMS uses the basic principal that detection of the TCU for aspecific patient allows all subsequent transitions and zones to beestimated with greater sensitivity and accuracy than using empiricaldata solely (as with prior art DOE systems).

General Overview for CALPAT Operation:

a) After ADMSSTART is selected the ADMS monitors patients AEPi, Bi andSSA.b) Empirical data values derived for typical (ref 1) conditions of TCUare compared to actual and real-time patient's data for AEPi, Bi andSSA.c) The weighting factor applied to each of AEP, Bi and SSA is dependenton the following factors.d) When the TCU (Transition A to B) has been identified for a specificpatient is derived.e) The TCU transition is noted in terms of the AEPi, Bi and SSA value.The noting of these corresponding TUC values, allows the accurateswitching and monitoring of AEPi and Bi subsequent to changingtransitions (A to B, B to C and C to D) and Display Zones (A, B, C, D).f) When the TCU (Transition A to B) has been identified for a specificpatient other transitions (B to C-TSW, and C to D-TCU) can then bederived from this calibration data.Deriving the Subsequent Transition States from the CALPAT.

TCU state is more sensitive and accurate for a given patient thanreliance on empirical data values for these subsequent transition states(B to CTSW, C to DTUC).

Sample pseudo code sample for CALPAT function (Psuedo code may beexpanded upon or deleted dependent upon whether the preferred embodimentdemands such detail).

-   -   a) Select STARTADMS=1% Wait till ADMSSTART button is selected    -   b) Assign defaults TCU, TSW and TUC values from empirical data        transition thresholds (3).

TABLE 5 SSA Conditions Zone Empirical Bi Empirical AEPi (Ref 34, 45, 46,113) Transition Transition-ref1 Transition-ref1 (ref STEP 16) Zone A toB (TCU) 76 65 CAW > S OR CA2W > S Zone B to C (TSW) 40 36 CA3W > S ZoneC to D (TUC) 74 50 CAW > S OR CA2W > S AEPi Bi Assign TCU 65 76 AssignTUC 50 74

-   -   d) Start CAP PAT procedure and determine patient specific values        for TCU and TUC.    -   e) Read Current Patient Data value for AEPi, Bi and SSAi % read        the real-time patient data and Compare this real patient TCU        data values to TCU Empirical Data values for AEPi, Bi and SSAi.    -   If Current Patient Data AEPi Value (CDAEPi) for Transition from        Consciousness to Unconsciousness (TCU) is less than or equal to        (<=) Empirical Data AEPi Values (IDAEPi) for Transition from        Consciousness to Unconsciousness (TCU) note the Current Data        values for Bi and SSAi.    -   The said CD values for Bi and SSA are assigned respectively to        variables for Calibrated Patient data for Transition from        Consciousness to Unconsciousness for Bi (CDTCUBi) and Calibrated        Patient data for Transition from Consciousness to        Unconsciousness for SSAi (CDTCUSSA).    -   f) Assign;

CPTCUBi CPTCUSSA

-   -   g) Transition states TSW and TUC are now derived from TCU.        Calibrated Patient data for Transition from Unconsciousness to        Consciousness for AEPi (CPTCUAEPi) will be derived from        CPTCUAEPi transition state, ie. CPTUCAEPi is proportional or        related to CPTCUAEPi.

Note that in more complex embodiments of the ADMS more complicatedcalibration of the patient's variables can be applied to provide agreater degree of patient sensitive system calibration.

Once the value for CPTCUBi is established per above, CPTUCBi can bederived (in a simple embodiment as described herein) by using the ratioderived from empirical data being IDTUCBi/IDTCUBi.

-   -   h) Assign value for CPTUCbi    -   CPTUCBi=IDTUCBi/IDTCUBi×CPTCUBi

Further embodiments can utilize a more sensitive formula based onapplying any combination of TCU, TSW and TUC derived from using patientAEPi, Bi and SSAi data to derive TSW and TUC.

PATCAL and Default Threshold Determination for TCU and TUC

The thresholds for TUC and TCU for BSAEPI and Bi can vary betweenpatients. The current ADMS sample embodiment assumes that therelationship between these TCU and TUC values is able to be derived(refer step 7, ADMS sample embodiment) from empirical data (Gajrag et al1999) and then modified for individual patient compliance with PATCALfunction (per step 5, ADMS sample embodiment). However, as the ADMSsystem further evolves, the means of providing a more accuratedetermination of the TCU and TUC thresholds may also evolve,particularly with increased clinical data and experience with thisdevice. Combinations of the following variables can assist the ADMS inpredicting more accurate default and PATCAL TCU and TUC values; BSAEPi,Bi, SSA, eyelid opening and movement status, eye movement status,arousal and body movement status.

Step 6. Is CALPAT Set?

If no go to Step 7 and if yes go to Step 9.

Step 7. Set to ADMS Default (Impirical Data) Display Zone Functions(DDZF). (DDZA, DDZB, DDZC, DDZD)

Empirical data values are referenced as a means of establishing thetransition of zones A, B, C and D based on data collected from normalpatients.

The empirical data values used for the purpose of this embodiment andsimplicity of presentation is set out in Tables 6 and 7 below (3).

TABLE 6 Zone Empirical AEPi Empirical Bi SSA Conditions TransitionTransition Transition (Ref 34, 45, 46, 113) Zone A 65 76 (W OR STG1) to(STG2 or to B(TCU) STG3) Zone B 36 40 STG2 or STG3 to 4 or to C(TSW) REMZone C 50 74 W or STG1 to STG2 or to D(TUC) STG3

TABLE 7 Zone Bi AEPi Ranges Range Range SSA Conditions ref Zone A 100-82100-65 W OR STG1 Zone B  82-40  65-35 STG2 or STG3 or STG4 or REM Zone C 40-75  35-50 STG2 or STG3 or STG4 or REM Zone D 75-100 50-100 W OR STG1

Determination of Switching or Weighting of BSAEPi and BI.

It has been reported (Gajrag et al 1999) that AEPi provides improveddetection of the transition from unconsciousness to consciousness (TUC).

This may be due to BSAEP reflecting the neural response to the auditorysensory nerve, as stimulated by the application of ADMS earphones clickstimulator, to a subject's ear and auditory nerve. An increase in theAEP signal can provide a sensitive measure of the audio sensory nervesresponse (or lack of in state of unconsciousness) with communicationpaths to the brain (BSAEP), and in particular the associatedvulnerability to incidence of audio recall.

The “switching on” or activation of the auditory BSAEP communicationpaths provides a more rapid signal change and subsequent measure oftransition state than that of BIC. BIC signal, in contrast, is a measureof overall brain activity and can incorporate a mixture of controlsignals for the body. These “mixture” of signals may not directly relateto the consciousness factors or factors effecting depth of anesthesiastatus, such as vulnerability or risk of post-operative memory recall.

In MODE 1 the ADMS is capable of referring to the SSA analysis and inparticular the patient's EEG spectral composition, to assess theprogression from one stage of unconsciousness (sleep) to a lighter stageof consciousness (sleep). This “independent” (from BSAEP and BIS)assessment of SSA, aids the arbitration process. Improved determinationof pending onset of the TUC transition can therefore be achieved withthe ability to apply closer analysis and measurement focus on the rapidincrease in the BSAEP signal (as would be expected with TUC).

Other MODES of the ADMS are capable of applying any combination ofBSAEPi, Bi, SSA, eyelid opening and movement status, eye movementstatus, arousal and body movement status, as a means of determiningswitching or weighting between Bi and BSAEPi.

Step 8. Set to ADMS Default (Impirical Data) Display Zone TransitionFunctions (IDZTF). (IDZTCU, IDZTWS, IDZTUC) Step 9. Set to ADMS CALPATDisplay Zone Functions (CPDZF). (CPDZA, CPDZB, CPDZC, CPDZD) Step 10.Set ADMS Display Zone Formulas A, B, C, D. to Calibrated Patient DisplayZone Transition Formulas (CPDZTF) Step 11. Set ADMS Alarm Thresholds

Display Zone Critical Alarms Thresholds (DZCAT) are defined

These DZCAT consist of alarm warnings and display notification ofparticular importance to ADMS user, including body arousal or movementfor example.

The DZCAT can be presented as markers on the CIAi display, alarms ofother forms of user notification to assist the ADMS operation.

BMi and Ai can be used to weight or bias the CIAi towards patientconsciousness state and/or represented as separate display, alarms ofother forms of user notification to assist the ADMS operation.

Step 12—AEPi Analysis (Ref 3, 61) Step 13—AEPI Analysis Display or PrintStep 14—Bi Analysis (Ref 3)

FIG. 3 shows a flow chart of one form of bicoherence, real tripleproduct and bispectral index analysis.

Computation of Bispectrum (B), Bicoherence and Real Triple Product

${B\left( {f\; 1f\; 2} \right)} = {{\sum\limits_{l = 1}^{L}{{{Xi}\left( {f\; 1} \right)}{{Xi}\left( {f\; 2} \right)}{{Xi}^{*}\left( {{fi} + {f\; 2}} \right)}}}}$

Epoch length=30 seconds75% overlap of epochs to reduce variance of bi-spectral estimateL=epochs, i.e. 1 minute of dataf1&f2 are frequency components in the FFT such that f1+f2≦fs/2 where fsis the sampling frequency

Real Triple Product (RTP)

${{RTP}\; \left( {}^{*}{f\; 1f\; 2} \right)} = {\sum\limits_{l = 1}^{L}{{{Pi}\left( {f\; 1} \right)}{{Pi}\left( {f\; 2} \right)}{{Pi}\left( {{f\; 1} + {f\; 2}} \right)}}}$

Where Pi(f1) is the power spectrum P(F)=|X(F)|²

Bi-Coherence (BIC)

${{BIC}\left( {f\; 1f\; 2} \right)} = \frac{100{B\left( {f\; 1f\; 2} \right)}}{\left. \sqrt{}{{RTP}\left( {f\; 1f\; 2} \right)} \right.}$

ranging from 0 to 100%

Step 15—Bi Analysis Display or Print Step 16—Sleep Staging Analysis(SSA) (34, 35, 45, 46)

FIG. 4 shows one form of sleep staging analysis. Referring to FIG. 4,the Sleep Staging Analysis (SSA) provides two data descriptions, beingthe context analysis (described below in the form of S1W>S etc) andsleep stage estimation of a subjects (derived) EEG, EOG and EMG data (inthe form of sleep stage as derived from spectral analysis of EEG andcorrelation of EMG and EOG signals (34, 45, 46). “Derived” in FIG. 5denotes that these signals may be direct electrode connections to thescalp for neurology, nears patient's eyes for EOG, near patients chin orcheek for EMG signals, or alternatively may be derived from a singleforehead (or forehead to chin area) electrode attachment.

If only forehead EEG electrodes are used, the EMG data will be derivedas muscle electrical amplitude from signal frequency response range of(typically 70 Hz to 150 Hz bandwidth).

The SSA outputs are utilized to determine the weighting analysis andtime of switching weighting analysis (STEP 23).

Sleep State Context

**Key for SSA (where sleep stages can be 1, 2, 3, 4, REM, WAKE)

STATE ANALYSIS TYPE CA1W > S Change from WAKE to (sleep-stage ref 34,35, 45, 46 1 OR 2 OR 3 OR 4 OR REM) CA2W > S Change from sleep-stage 1to ref 34, 35, 45, 46 (2 OR 3 OR 4 OR REM) CA3W > S Change fromsleep-stage 2 to ref 34, 35, 45, 46 (3 OR 4 OR REM) CA4W > S Change fromsleep-stage 3 to ref 34, 35, 45, 46 (4 OR REM) CA5W > S Change fromsleep-stage 4 to REM) ref 34, 35, 45, 46 CA6S > W Change fromsleep-stage REM to ref 34, 35, 45, 46 (WAKE OR 1 OR 2 OR 3 OR 4) CA7S >W Change from sleep-stage 4 to ref 34, 35, 45, 46 (WAKE or 1 OR 2 OR 3)CA8S > W Change from sleep-stage 3 to ref 34, 35, 45, 46 (WAKE OR 1 OR2) CA9S > W Change from sleep-stage 2 to ref 34, 35, 45, 46 (WAKE OR 1)CA10S > W Change from sleep-stage 1 to WAKE ref 34, 35, 45, 46

For simplification and minimal electrode attachments to patient Ai canbe derived from existent EEG forehead (B or AEP) electrodes.

Step 17—Sleep Stage Analysis (SSA) Display or Print Step 18—BodyMovement Index (BMi) Analysis

BM detection may be by way of analysis from a mattress movement sensordevice or other pressure or movement sensitive sensors/electrodesattached to the patient. Detection of Body Movement (BM) relates to aphysical movement of the body such as detected by a pressure orvibration sensitive sensors.

Step 19—Body Movement Index (BMi) Analysis Display or Print Step20—Arousal Index (Ai) Analysis (35) Step 21—Arousal Index (Ai) AnalysisDisplay or Print Step 22—Display Zone Transition Formula (DZTF) Step23—Set Analysis Arbitration, Weighting and Timing

This step defines the weighting ratios together with timing of changesof the weighting ratios of AEPi and Bi for each of the zones A, B, C D.for the ADMS Comprehensive & Integrated depth of Anesthesia index (CIAO.

FIG. 5 shows a block diagram of an overview of analysis associated withweighting index. The abbreviations TF and OS in FIG. 5 are defined asfollows. TF=Transfer Formula. The transfer formula is designed toprovide an adjustment or normalization of index values in order to allowall analysis input data to be comparable and allow cross-selectionwithin the Weighting Analysis Block without mismatching or obvious leveljumps, when switching between AEP, Bi or SSA analysis.

OS=Offset. The Offset is designed to provide an offset adjustmentbetween AEPi, Bi and SSAi in order to avoid level jumps when switchingbetween AEPI, Bi and SSA.

FIG. 6 shows a flow diagram associated with AEP:BIC analysis weightingarbitration (Mode 1). The abbreviation S in FIG. 6 denotes a step.

The system of the present invention may allow the user to readilyupgrade the system's logic and accuracy with the course of time and moreadvanced ADMS clinical data. The ADMS system may include a self-learningcapability to evaluate any selected group of studies and via analysis ofthese studies allow ADMS system weighting and analysis priorities tochange in accordance with more developed clinical data studies.

As detailed in the steps of FIG. 6 the ADMS system highlights to thesystem user 4 main zones of interest, while monitoring a patient undergeneral anesthesia, as detailed in Table 9 below. The following codesare used in Table 9.

Definition of Zones A, B, C, D CODE Zone Ranges Key Description Zone ACU Patient emerging from Consciousness to Unconsciousness. Zone B UPatient in unconscious state. Zone C U Patient in unconscious state.Zone D UC Patient Transition from Unconsciousness to Consciousness.

Table 9 presents examples of ADMS modes of operation. The ADMS mayprovide a capability for weighting ratios to be changed or programmed byADMS system researchers or for a range of pre-configured weightingratios (MODES) to be selected.

TABLE 9 AEP Bi SSAi AEP Bi SSAi Zone RATIO RATIO RATIO RATIO RATIO RATIOMODE 1 MODE 2 A 100 0 0 80 20 0 B 0 100 0 20 100 0 C 0 100 0 20 80 D 1000 0 80 20 MODE 3 MODE 1 + N 100 0 0 B 0 50 50 C 0 50 50 D 100 0 0 NOTE:The range of MODES may be selected in accordance with patient or medicalprocedure related factors. For simplicity a simple MODE 1 configurationis presented as an example of an ADMS embodiment. N + 1 Mode representsa large library of Modes which may be selected or programmed into theADMS system.

Step 24—Scaling and Transition Function

The scaling/range and transition functions are designed to provide amethod of scaling inputs to the CIAi to minimise confusion or errorassociated with ADMS operation. In particular this confusion or errorcan occur if the two scales and ranges of BICi and AEPi (for example)are not compatible, or in a data format suitable to be combined anddisplayed as a single CIAi.

Scaling and range of AEPi and BICi refers to a change or adjustment ofcalculated values of AEPi and BICi (as detailed in steps 14 and 12)respectively, to “match” the 2 separate indices so that when weightingor switch changes occur, the CIAi does not have a sudden jump orconfusing change in value or scale representation.

The switch transition function may adjust the time duration over whichany switch or weighting change occurs between (for example) BICi andAEPi. Furthermore the transfer function applied to each of therespective data inputs (BICi and AEPi, for example) during this switchover duration or period may be selected from a range or transferfunctions. However, as with the scaling factor the default transferfunction will be X1 (linear).

The diagram shown in FIG. 7 presents a simplified overview of scalingfactor and transition curve functions associated with the ADMS.

Step 25—Display Comprehensive Integrated ADMS Index (CIAi) MODE 1 CIAiBasic Assumptions

1. MODE 1 presents one of the simplest embodiments of the ADMS.2. Table 10 below summarizes the weighting factors for zones A, B, C andD.3. The column entitled Display transition includes a column titleddisplay offset. This value is designed to minimize level changes duringthe switching of AEPi: Bi weighting from 100:0 to 0:100.

TABLE 10 CIAi CIA Display Translation formula IDO Display Zone AEP:Biratio Transfer function IDO (offset) A 100:0 X1 0 0 B   0:100 X1−(76-65) −11 C   0:100 X! −(76-65) −11 D 100:0 X! (74-50) − 11 13 NOTE1: Offset code IDOA AEPi IDOB -Empirical Data Offset applied for zone B= -(Bi-AEPi); for values end of first consciousness period (ref 1, FIG.5). IDOC -Empirical Data Offset applied for zone C = -(Bi-AEPi); forvalues at end of first consciousness period (ref 1, FIG. 5). IDOD-Empirical Data Offset applied for zone D = (Bi-AEPi; for values atstart of second consciousness period (ref 3, FIG. 5))-IDOC NOTE 2:Values at end and start of conscious periods (TCU and TUC respectively)and TSW (ref 3, FIG. 5) are set out below.

TABLE 11 Transition AEPi Value Bi Value TCU 65 76 TSW 36 40 TUC 50 74

TABLE 12 Time DZ AEPi Bi Ai BMi DZTF AEPi:Bi CIAi t0 = start Display **** * * assume = Ratio *** t10 = end Zone X 1 ref: 1 See Ref. 1 Ref. 1ref: Step 22 See See Step 8 Step 23 Step 24 COL 1 COL 2 COL 3 COL 4 COL5 COL 6 COL 7 COL 8 COL 9 t1 A 77 85 81 79 (X1) 100:0 77 t2 A 76 90 7375 (X1) 100:0 76 IDC (mean) 75 90 70 65 (X1) 100:0 75 IDTCU A 65 76 8064 (X1) 100:0 65 IDU (mean) 37 49 43 45 (X 1) − 11   0:100 38 t3 B 35 4235 38 (X 1) − 11   0:100 31 t4 B 34 41 36 36 (X 1) − 11   0:100 30 t5 C35 38 36 37 (X 1) − 11   0:100 27 IDTSW 36 40 38 37 (X 1) − 11   0:10029 t6 C 40 52 41 38 (X 1) − 11   0:100 41 t7 C 40 62 42 39 (X 1) − 11  0:100 51 t8 C 39 71 43 40 (X 1) − 11   0:100 60 IDTUC 50 74 40 38(X1) + 13   0:100 87 t9 D 60 75 50 60 (X1) + 13 100:0 88 t10 D 77 80 7775 (X1) + 13 100:0 93 * Data presented for sample only ** The ID valuespresent some ambiguity, particularly in relation to IDTCU, IDC (mean),IDU (mean), IDTSW and IDTUC values. However the selected ID values aredesigned for update with clinical data studies, currently in progress(Reference 3). *** CIAi formula; (AEPi (column 3) × AEPi ratio (column 8AEPi ratio value)) + (cont'd) (Bi (column 4) × Bi ratio (column 8 Biratio value)) + DZTF (Column 7) = CIAi Note: AEPI/Bi or Bi/AEPinumerator and denominator are taken from respective AEPi and Bi ratiovalues per column 8 in Table 12.

FIG. 8 shows a graphical representation of CIAi, TCU & TUC values;

FIG. 9 shows a graphical representation of absolute value of Ai+Bi, TCU& TUC;

FIG. 10 shows a graphical representation of AEPi;

FIG. 11 shows a graphical representation of Bi;

FIG. 12 shows a graphical representation of Ai;

FIG. 13 shows a graphical representation of BMi;

FIG. 14 shows a graphical representation of Bi with the colour of thebackground changing to indicate transition of consciousness state.

NOTE 1. Sleep Staging Analysis Step 16 and Analysis Weighting Step 23are simplest embodiments as operated in MODE 1. However, the currentstate of clinical data (ref 11) provides only a slight correlationbetween bispectral values of EEG and conventional sleep staging. Moreadvanced embodiments of the ADMS will provide greater definition andspecifications in relation to spectral based sleep analysis (ref 3, 8,9, 11). These further MODES will, in particular, deploy modifiedfrequency distribution as opposed to the conventional frequency andamplitude analysis for sleep stage definition.

NOTE 2: Values at end and start of conscious periods (TCU and TUCrespectively) and TSW (ref3, FIG. 5) are set out below.

TABLE 13 Transition AEPi Value Bi Value TCU 65 76 TSW 36 40 TUC 50 74

TABLE 14 Time DZ AEPi Bi Ai BMi DZTF AEPi:Bi t0 = start Display * *assume = Ratio t10 = end Zone X1 ref: 1 ref: Step ref: Step ref: Step 14ref: Step 22 ref: Step 23 t1 A 76 85 81 79 1 100:0   IDTCU-60 A 75 82 8079 1 100:0   t2 A 75 90 73 75 1 100:0   IDC (mean) 75 90 60 65 1 100:0  IDU (mean) 40 49 43 45 1 0:100 t3 B 35 42 35 38 1 0:100 t4 B 31 41 36 361 0:100 t5 C 35 38 36 37 1 0:100 IDTSW 35 38 38 37 0:100 IDTUC 40 44 4038 0:100 t6 C 40 52 41 38 1 0:100

FIG. 15 shows a flow chart of an improved system for monitoring indicesassociated with human consciousness and incorporating artifactrejection.

FIG. 16 shows a simplified functional system overview (FSO) of apreferred embodiment of apparatus according to the present invention.The apparatus of FIG. 16 is a Monitoring and Diagnostic Systemincorporating a reduced risk Depth of Anaesthesia Analysis andMonitoring System, including Minimal Sensor-Electrode attachments forConsciousness, Audio Sensory, Movement/Arousal/Muscle Activity, EyeMovement/Opening, Stress/Anxiety/Vital Signs Parameters, andAudio-Visual Recall.

FIG. 17 shows a more detailed functional system overview (MDFSO) of apreferred embodiment of apparatus according to the present invention.The apparatus of FIG. 17 is a Depth of Anaesthesia Analysis andMonitoring System, incorporating an extended range of Sensor-Electrodeattachments for Consciousness, Audio Sensory, Movement/Arousal/MuscleActivity, Eye Movement/Opening, Stress/Anxiety/Vital Signs Parameters,and Audio-Visual Recall, audio, video, PTT, activity sensor, bloodpressure, oximeter, body and head wireless electrode modules.

Referring to FIG. 16, the apparatus of the HCM system includes aelectrode-sensor system (Block 1) connected to a signal conditioning anddata acquisition system (Block 4), an analysis and monitoring system(Block 3) and a user display and optional touch screen operatorinterface system (Block 2). Block 5 provides means for time stampedvideo and audio to be recorded.

General Overview of Human Consciousness Monitoring System IncorporatesDrawing FIGS. 16, 17, 35, 34, 43.

Block 1 in FIG. 16 presents that sensors and electrodes are connected tothe patient body by means of a unique integrated electrode system (referFIG. 35). The latter provides use of wireless electrode systems andspecial self-adhesive electrode attachment systems to achieve aminimally invasive and simple tangle-free patient connection system,desirable for anaesthesia application.

EEG electrodes are for simultaneously monitoring EEG physiological datafor optimised bi-spectral and optimised Sleep/Wake analysis, EOGelectrodes are for Sleep/Wake analysis, Audio Sensory Electrodes are formonitoring auditory evoked potential from a patient's auditory sensorynerve (refer Block 11 in FIG. 18), Reference Electrodes are forreference of electro-physiological signals, Chin EMG electrodes are forarousal and sleep/wake analysis and redundant or backup electrodes canbe applied with various embodiments (refer FIGS. 35, 37, 34, 17). Audiostimulation can optionally be applied by means of a wireless linkedpatient earpiece, to minimise wiring.

The apparatus may be configured by the user for different modes ofoperation and furthermore is designed in a modular fashion to allowvarying degrees of complexity and versatility. The most complex versionof the system is configured to accommodate monitoring and analysis of abroad range of physiological parameters, as detailed above, while morebasic versions can be configured to accommodate critical parameters suchas “sleep-wake” analysis (34, 45, 46), bi-coherence analysis, audioevoked potential and arousal analysis. “Sleep-wake” analysis, forexample, may be applied to optimise appropriate weighting between audioevoked potential and bi-coherence analysis in determining a subject'sconsciousness.

Electrode high-impedance amplifiers, signal conditioning and audiovisualmonitoring and recording functions (refer Blocks 2, 3 and 4 in FIG. 16)are provided by devices such as Compumedics Siesta, E-Series andProfusion software (71, 72, 73). The aforementioned devices aresupplemented with specialized sensors (refer Block 1 in FIG. 16) such asaddition of minimally invasive wireless and integrated functionelectrode and sensor systems (refer FIGS. 35,34). Time synchronizedaudio-visual capability of the apparatus (refer Block 5 in FIG. 16) isfurther detailed in FIG. 43.

Basic electrode amplification requires medical grade isolation, withspecial additional input circuitry for electrosurgery protection and RFinput filtering for protection against extreme conditions of voltage asmay occur with defibrillation procedures which are possible in acritical monitoring environment of an operating theatre, being thelikely application environment for the apparatus.

Overview of Types of Physiological Sensory Monitoring Parameters andAnalysis for Depth of Anaesthesia Application and Usefulness.

The apparatus provides electrode-sensor attachment capability to apatient and includes a capability, with use of integrated and wirelesselectrodes (refer FIGS. 33,34,35,37) to provide a comprehensiveassessment of a patient's physiological states via monitoring andanalysis of the patient's critical sensory systems (critical includesavoiding incidence of recall or premature anaesthesia awakening), whilethe patient is undergoing anaesthesia drug delivery. Comprehensiveassessment of human sensory systems includes consciousness (bi-coherence& Sleep/wake. Audio sensory (AEP analysis), arousal sensory (arousal,micro-arousal and movement states), eye opening (special EOS), anxiety &stress state and vital signs (Blood pressure, temperature, GSR, HR andoxygen saturation). Furthermore the apparatus provides a means ofrecording patient and operating environment audio and video with timesynchronisation link to patient physiological parameters, thus providingevidence for legal implications such as claims made relating topremature depth of anaesthesia wakening or for physiological recallpurposes. Block 5 in FIG. 16 presents that audio and video can berecorded in time synchronisation with the depth of anaesthesiamonitoring procedure, providing an important evidence record. This maybe particularly important for verifying audio recall or other type ofclaims by subjects undergoing depth of anaesthesia monitoring.

FIG. 18 shows a main flow diagram (MFD) of the HCM system according to apreferred embodiment of the present invention. Patient physiologicalparameters are signal conditioned and digitised in Block 4 of FIG. 16.The digitised signals are read or buffered in Block 3 of FIG. 18. Datais stored in Block 3 of FIG. 18 in buffer sizes based on filter andanalysis requirements. Data from Block 3 is applied to Digital Filteringin Block 40. Block 40 provides filtering for various physiological datachannels. Block 40 is also linked to signal validation Block 7 toprovide a means of compensating for poor signal conditions, such asexcessive mains interference noise, which may require notch filtering at50 or 60 Hz depending on the mains frequency in the country of operationof the apparatus.

Data from Block 40 is also linked to Analysis Format Block 8 to providespecialised filtering where signals required for analysis may need to besubstituted by selected alternative signals. This could occur, forexample, where Sleep/Wake analysis is required and no C3 electrode scalpsignal is available but outer malbar forehead signals may instead needto be optimised with digital filtering to provide the closest possibleemulation of C3 EEG signal format.

Filtered signals from Block 40 are validated in Block 7, where eachsignal is characterised and checked for a range of potential errors,artefact and corruption. The validation of each signal allows the HCMsystem to present a signal validation score for each signal, so that theuser can be prompted when erroneous or unreliable signals couldadversely affect the systems output state determination.

This type of method provides an early warning and error reduction forcritical monitoring and analysis in a depth of anaesthesia system, whichotherwise could be more vulnerable to ambiguous outcomes of patientstate determination.

The following provides a more detailed overview of types ofphysiological parameters, weighting and data translation and combiningof analysed parameters for presentation of Integrated or Combined Indexto provide desired functional output and achieve useful application ofthe HCM system and useful apparatus for depth of anaesthesia monitoring.

The following section details how the apparatus has a capability to takesensory physiological parameters including consciousness, audio,arousal-movement, eye movement-opening, stress-anxiety and vital signsparameters, and apply weighting and combining techniques to theseparameters to provide a user friendly and risk minimised depth ofconsciousness monitoring and analysis device. For ease of presentationthis overview will proceed in the order of physiological parameters setout above.

The apparatus is capable of monitoring electroencephalographicphysiological parameters to provide neurological based analysis foroptimised bi-spectral analysis patient state and optimised R&Ksleep-wake patient states. The physiological parameters for bi-spectrumvalues are the outer malbar EEG electrode connections to the patient'sforehead together with A1 or A2 EEG mastoid reference connections.

EEG signals are analysed in Block 10 of FIG. 18 whereupon thebi-spectrum, bi-coherence and real triple products are derived.

In accordance with empirical clinical data results (initially set withfactory default values) the weighting for column 1 of table DCTTpresents bands of bi-spectrum values between value 0 and 100, where thebi-spectrum values refer to between above mentioned bi-coherence andtriple product and bi-spectral index together with empirical clinicaldata results calculate and determine these 0 to 100 values.

Column 2 of table DCTT presents Consciousness To UnconsciousnessTransition Thresholds (CTUT) Negative Slope, for the BIC or bi-spectrumvalues critical threshold values of the bi-spectrum value normalisedbetween values of 1 to 100 (bi-spectrum value is determined frombi-coherence, triple product and optimisation of these parameters withempirical data results).

Column 3 of table DCTT presents Unconsciousness To ConsciousnessTransition Thresholds (UCTCT) Negative Slope, for the BIC or bi-spectrumvalues critical threshold values of the bi-spectrum value normalisedbetween values of 1 to 100 (where the bi-spectrum value is determinedfrom bi-coherence, triple product and optimisation of these parameterswith empirical data results). An object of the HCM system is todistinguish between the transition of consciousness to unconsciousnessand visa versa and to apply critical threshold detection and weightingvalues to the analysis data in accordance with the transition. In thisway the apparatus optimises visual display tracking of a subject's depthof anaesthesia to reduce risk of interpretation of state determinationof a monitored patient. “Positive” and “negative” as used in thiscontext has similar meaning throughout this document.

Column 4 of table DCTT presents the weighting values which are appliedto optimised bi-spectral analysis (0-100 normalised values) to amplifythe critical area of the display graph for bi-spectrum display and alsoto achieve a visual affect so that all sensory displays (consciousness,audio, arousal-movement, eye movement-opening, stress-anxiety and vitalsigns) appear to be visually aligned so that when all sensory andcombined sensory index's are operating with optimal zone system the userhas a simple visual alignment of various graph displays. These weightingfactors are indicated as sample factory default values, but this is onlyindicative as the means of system to weight these parameters is achievedby allowing the apparatus to be modified and upgraded by varioustechniques including any form of network access, smart card or otherremovable storage device or specially authorised user system access andconfiguration.

Alignment of critical thresholds and optimal working area is an objectof the HCM system, as the user has a uncomplicated method of ensuringthat concentration and delivery of anaesthetic agent does not cause thedisplay metering of the depth of anaesthesia monitor to move outside theoptimal area of operation. Furthermore the display graphs associatedwith each sensory parameter and the combined index change colour to say,green when operating within the optimal area and orange when operatingoutside the optimal area, for example.

In a busy and stressful operating theatre these operational and useraspects may make a substantial difference to the useability of theapparatus. The apparatus may improve accurate assessment of rate of andconcentration of, anaesthetic drug administration during depth ofanaesthesia monitoring.

Column 5 of table DCTT presents Unconsciousness To ConsciousnessTransition Thresholds (UCTCT) Positive Slope, for the BIC or bi-spectrumvalues critical threshold values of the bi-spectrum value normalisedbetween values of 1 to 100 (where bi-spectrum value is determined frombi-coherence, triple product and optimisation of these parameters withempirical data results).

An example of bi-spectrum values and weighting in accordance with theabove detailed formats and processing are presented in table DCTT column6 (sample bi-spectrum data), column 7 (weighting or translation valuesapplied to the bi-spectrum values), column 8 (un-normalised bi-spectrumvalues) and column 9 (bi-spectrum values normalised between 1 and 100).

In the system's minimum and preferred configuration (for reasons ofsimplicity) a single pair of EEG electrodes attached to a subject'sforehead (A1, A2 outer malbar bone skin surface positions) is monitoredand analysed to produce a bi-spectral index (derivation of bi-coherenceanalysis) and also subjected to spectral analysis with artefactrejection techniques to produce an estimation of sleep state based onR&K rules but with compromised signal locations. Compromised electrodelocations refer to applying forehead A1 and A2 outer malbar electrodepositions as opposed to the clinical standard. (refer Principles andPractice of Sleep Medicine—Kryger Roth and Dement Roth) instead of thetypical A3 (requires specialised scalp electrode application).

The apparatus has a capability to present reports and analysis displayand reports in a simple condensed tabular or graphic form, or moredetailed reports and displays detailing raw or basic physiological data.In this way expedient and effective validation of condensed raw dataresults is accessible to the user. Furthermore graphic and condenseddisplay graphs provide a means of combining or integrating variouscombinations of consciousness input monitoring variables (including oneor more of the various sensory monitored inputs). In this way the userhas a capability of combining sets of consciousness index including forexample bi-spectral analysis combined with audio-evoked potentialanalysis, arousal analysis combined with bi-spectral and audio evokedpotential analysis, amongst other combinations of analysis andsubsequent index measures.

Note 1: Any combination of 1, 2, 3, 4 and 5 can be utilized for displaypurposes.

Note 2: 1, 2, 3 4, 5 and 6 represent analysis outputs for BIC, AEP,Arousal, Eye opening and movement, anxiety andsleep-unconsciousness/wake-consciousness respectively.

Note 3: A, B, C, D, E represents analysis data after critical thresholddetection, Display data translation and display normalization.

Block 7—FIG. 18

SIGNAL VALIDATION Raw Data Frequency Actual Actual set Value Pass bandSignal Range High Pass Impedance Impedance a) Impedance DistortionSignal Group Electrode (MilliVolts) Low Pass Measure Weight NormalisedMeasure Channel Type Type Placement or per unit (Hz) Value Factor 1-10Value Value SIGNAL CONFIGURATION AND TABLE REFERENCES 1 EEG R&K C30-.300 0.3-30 1Imped-1 2 EOG R&K Left eye 0-.300 0.3-30 1Imped-1 3 EOGR&K Right eye 0-.300 0.3-30 1Imped-1 4 EMG R&K subment 0-.260 0.3-301Imped-1 5 EMG R&K selectEMG 0-.260 0.3-30 1Imped-1 6 EEG BIC Fp1 0-.3000.3-30 1Imped-1 7 EEG BIC Fp2 0-.300 0.3-30 1Imped-1 8 EEG BIC Fpz0-.300 0.3-30 1Imped-1 9 EEG AEP Mastoid+ 0-.300  70-260 1Imped-1 10 EEGAEP mid-foreh− 0-.300  70-260 1Imped-1 11 EMG EP L-EP+ 0-.260  70-2601Imped-1 12 EMG EP L-EP− 0-.260  70-260 1Imped-1 13 EYE TRK EYE-LID +0-500 .01-15 1Peizo-1 14 EYE TRK EYE-LID − 0-500 .01-15 1Peizo-1 15 ECGVital-Signs 0-5 .03-30 1Imped-1 16 Sa02-HR Vital-Signs BPM NA SAO2-1 17Sa02 Vital-Signs 0-100% NA SAO2-1 18 SAO2-PTT Vital-Signs arous/min NASAO2-1 19 BloodPres Vital-Signs 0-300 mmHg NA Distortion b) DistortionDC-Offset DC-Offset c) DC-Offset Dc Stability Dc Stability d) DcStability Signal Weight Normalised Measure Weight Normalised MeasureWeight Normalised Channel Type Factor 1-10 Value Value Factor 1-10 ValueValue Factor 1-10 Value 1 EEG Distn-1 DC-Offset 1 1 0-10 DC-Stab1 2 EOGDistn-1 DC-Offset 1 0-10 DC-Stab1 3 EOG Distn-1 DC-Offset 1 0-10DC-Stab1 4 EMG Distn-1 DC-Offset 1 0-10 DC-Stab1 5 EMG Distn-1 DC-Offset1 0-10 DC-Stab1 6 EEG Distn-1 DC-Offset 1 0-10 DC-Stab1 7 EEG Distn-1DC-Offset 1 0-10 DC-Stab1 8 EEG Distn-1 DC-Offset 1 0-10 DC-Stab1 9 EEGDistn-1 DC-Offset 1 0-10 DC-Stab1 10 EEG Distn-1 DC-Offset 1 0-10DC-Stab1 11 EMG Distn-1 DC-Offset 1 0-10 DC-Stab1 12 EMG Distn-1DC-Offset 1 0-10 DC-Stab1 13 EYE TRK NA NA 0-10 NA 14 EYE TRK NA NA 0-10NA 15 ECG NA NA 0-10 NA 16 Sa02-HR 17 Sa02 18 SAO2-PTT 19 BloodPresAmp-headr Amp-headr e) Amp-headr Mains int. Mains int. f) Mains int.Sig/Noise Sig/Noise Signal Measure Weight Normalised Measure WeightNormalised Measure Weight Channel Type Value Factor 1-10 Value ValueFactor 1-10 Value Value Factor 1 EEG Amp-Head1 Mains-Int1 S/N-1 2 EOGAmp-Head1 Mains-Int1 S/N-1 3 EOG Amp-Head1 Mains-Int1 S/N-1 4 EMGAmp-Head1 Mains-Int1 S/N-1 5 EMG Amp-Head1 Mains-Int1 S/N-1 6 EEGAmp-Head1 Mains-Int1 S/N-1 7 EEG Amp-Head1 Mains-Int1 S/N-1 8 EEGAmp-Head1 Mains-Int1 S/N-1 9 EEG Amp-Head1 Mains-Int1 S/N-1 10 EEGAmp-Head1 Mains-Int1 S/N-1 11 EMG Amp-Head1 Mains-Int1 S/N-1 12 EMGAmp-Head1 Mains-Int1 S/N-1 13 EYE TRK NA NA NA 14 EYE TRK NA NA NA 15ECG NA NA NA 16 Sa02-HR 17 Sa02 18 SAO2-PTT 19 BloodPres Signal Validityg) Sig/Noise Filters NB 30 Signal Normalised Actual Filters h) Filterssample Channel Type 1-10 Value Settings Recom. Alarm formula 1 EEGFilt-1 2 EOG Filt-1 3 EOG Filt-1 4 EMG Filt-1 5 EMG Filt-1 6 EEG Filt-17 EEG Filt-1 8 EEG Filt-1 9 EEG Filt-1 10 EEG Filt-1 11 EMG Filt-1 12EMG Filt-1 13 EYE TRK NA 14 EYE TRK NA 15 ECG NA 16 Sa02-HR 17 Sa02 18Sa02-PTT 19 BloodPres NB 1- Valid Impedance Table NB 2- NA = NotApplicable NB 3- KEY a) Impedance normalised 1-10 value b) Distortionnormalised 1-10 value c) DC Off-set normalised 1-10 value d) DCstability normalised 1-10 value e) Amp-Headroom normalised 1-10 value f)Mains Interference normalised 1-10 value g) Signal to Noise normalised1-10 value NB 4 For current channel mark as Valid if (a > A)&(b >B)&(c > C)&(d > D)&(e > E)&(f > F)&(g > G)- see NB3The following table is set-up in system configuration options

Valid Table Number Imped-1 Imped-1 Valid Table Number Peizo-1 ValidTable Name Electro-Impedance Valid Table Name Eye Track-Validate SignalEEG, EOG, EMG, ECG Signal Eye Track Sensor Groups Electro Groups EyePiezo Impedance Weighted Impedance Weighted Value (K) Value Value (K)Value 1 to 10 4 100K-200K 3 10 to 15 3 201K-300K 2 15 to 25 2 >300K1 >25 1 Valid Table Number SAO2-1 Valid Table Number Distn-1 Valid TableName SAO2 Valid Table Name Electro-Impedance Signal SaO2 Signal EEG,EOG, EMG, ECG Groups SaO2 Groups Electro DC Weighted Distortion WeightedValue (V) Value Value (%) Value 0-1 3 1 <1 or >1 1 2 3 >3 Valid TableNumber DC-Offset 1 Valid Table Number DC-Stab1 Valid Table NameDC-Offset Valid Table Name DC-stability Signal ElectrophysiologicalSignal Electrophysiological Groups Electro Groups Electro DC Weighted DCWeighted Value (mV) Value Value (mV) Value 1-100 mV 4 1-100 mV 1-200 mV3 1-200 mV 200-300 mV 2 200-300 mV >300 mV 1 >300 mV Valid Table NumberAmp-Head1 Valid Table Number Mains-Int1 Valid Table Name Amp-Head1 ValidTable Name Mains-Interference Signal Electrophysiological SignalElectrophysiological Groups Electro Groups Electro DC Weighted DCWeighted Value (mV) Value Value (dB) Value No-Clip 4 Bn <20 Clip+ 320-30 Clip− 2 30-40 Clip + & − 1 >40 1 Valid Table Number S/N-1 ValidTable Number Filt-1 Valid Table Name Signal to Noise Valid Table NameFilters Signal Electrophysiological Signal Electrophysiological GroupsElectro Groups Electro DC Weighted Deviation from rec. Weighted Value(mV) Value Value (% Hz) Value >40 4 HP > 20 30-40 3 EEG amp HP 0-2020-30 2 LP > 20 <20 1 LP 0-20 Ref: 3.2 Analysis Weighting Table SeeTable 2Output Complexity levels 1, 2, 3 and 4

Signal Validation

Provides a means for Automatic signal validation of a subject'smonitored variables by way of automatic impedance measurement, frequencyresponse, mains interference, signal to noise and signal distortioncharacteristics as part of the analysis algorithm for monitoring,detection or prediction of a subject's state of consciousness, sedationor vigilance.

Patient Calibration

Provides a means for a patient's calibration data to be utilised inanalysis algorithm for monitoring, detection or prediction of asubject's state of consciousness, sedation or vigilance.

Analysis Validation

Provides a means for Automatic Analysis Adaptation linked to signalvalidation. Where the analysis types are determined in accordance tostatus and quality of patient signals being monitored.

Automatic determination of available analysis processes by way ofvalidating input signal quality and activating analysis only inaccordance to validated signal sets associated with the analysis.

Once analysis types have been activated, weighting techniques areapplied to apply optimal emphasis for each analysis type. Furthermorevarious analysis types are combined to simplify the display method oftracking, prediction or detection of consciousness, sedation level or asubject's vigilance.

Analysis Format

Provides a means for Automatic Analysis format linked to signalsconnected, such as in the case of sleep and wake analysis where theanalysis parameters applied will depend on the validated signals. If,for example, only EEG outer malbar electrodes are validated, thenfrequency optimised EEG outer malbar signals can be utilised foranalysis, as opposed to more complex analysis signal combinationsincluding EMG and EOG signals.

Furthermore, weighting associated with each analysis type will depend onthe complexity and signal types available for each analysis type.

Analysis

Incorporates an integrated BIC and AEP algorithm, predicting EEGamplitude, integration of frequency (95% spectral edge, FFT) and, ½period amplitude analysis.

By utilising sleep and wake state determination as a means of contextanalysis to assist in determining which analysis method, from 5 or moremethods (auditory evoked potential (AEP) index (a numerical indexderived from the AEP), 95% spectral edge frequency (SEF), medianfrequency (MF) and the coherence (CHI) and R&K sleep staging) is mostsuitable for optimal accuracy off tracking each phase of the humanvigilance stages.

Provides a means for Localised Evoked potential analysis to detectmuscle or nerve response to incisions during localised or gas deliveredanaesthetic drug administration.

Provides a means for Eyelid tracking for vigilance monitoring anddetection with wireless electrode option. A further option exists usingself-applied electrodes where the electrodes consist of a low costdisposable component and a more expensive reusable component.

Patient Information

Provides a means for a patient's body Mass Index, age, medical historyand other relevant information to be utilised in an analysis algorithmfor monitoring, detection or prediction of a subject's state ofconsciousness, sedation or vigilance.

BIC Vigilance Application

Provides a means for BIC analysis for vehicle and machine operatorvigilance detection with wireless electrode option. A further optionexists using self-applied electrodes where the said electrodes consistof a low cost disposable component and a more expensive reusablecomponent. The said EEG monitoring can be by way of self-appliedwireless or headrest attached electrodes.

Block 7, Example

Example of Signal Validation Presenting Logic Example, BehindDetermination of Validation or Reliability Level of Various Sets ofPhysiological Data States, for Purpose of R&K Sleep-Wake StateDetermination. This Validation Level K can be Displayed for Purpose ofProviding the System User a Confidence Level of Analysis Monitoring andDisplay.

Example of Sleep Staging Signals Validity and Weighting

R&K Signals Weighting factor H-High, L L L M L M L H L M L H M M M HL-Low, M-Medium EEG-C3 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 EMG 0 0 1 1 0 0 11 0 0 1 1 0 0 1 1 EOG 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 BIC 0 0 0 0 0 0 00 1 1 1 1 1 1 1 1 Note: 1 = Signal Valid 0 = Signal not valid X = do notcare

Block 8—FIG. 18 Analysis Format Example Presenting Eeg Format AnalysisDetermination in Preparation for Sleep/Wake Analysis Per Block 18.

An Analysis format Validation Flow Diagram is shown in FIG. 19 based onanalysis confidence level, signal combination and analysis crosscheck.

Actual Actual Actual Actual Actual Analysis Analysis Analysis AnalysisAnalysis Options Options Options Options Options Level 1 Level 2 Level 1Level 2 Level 3 Signal BIC BIC R&K R&K R&K Signal Group Electrode WeightWeight Weight Weight Weight Channel Type Type Placement Value = 10 Value= 5 Value = 10 Value = 5 Value = 3 1 EEG R&K, NB1, 18 C3 YES YES YES YES2 EOG R&K, NB18 Left eye YES YES 3 EOG R&K, NB18 Right eye YES YES 4 EMGR&K, NB18 subment YES 5 EMG R&K, NB18 selectEMG YES 6 EEG BIC, NB18 Fp1YES YES 7 EEG BIC, NB1, 18 Fp2 YES YES 8 EEG BIC, NB1, 18 Fpz YES YESYES 9 EEG AEP Mastoid+ 10 EEG AEP mid-foreh− 11 EMG EP L-EP+ 13 EMG EPL-EP− 14 EYE TRK EYE-LID + 15 EYE TRK EYE-LID − 16 ECG Vital-Signs 17SAO2-HR Vital-Signs 18 SAO2 Vital-Signs 19 SAO2-PTT Vital-Signs 20BloodPres Vital-Signs Actual Actual Actual Actual Actual AnalysisAnalysis Analysis Analysis Analysis Options Options Options OptionsOptions Level 1 Level 2 Level 3 Level 4 Level 5 Signal Arousal ArousalArousal Arousal Arousal Signal Group Electrode Weight Weight WeightWeight Weight Channel Type Type Placement Value = 10 Value = 9 Value = 8Value = 7 Value = 6 1 EEG R&K, NB1, 18 C3 YES YES YES YES 2 EOG R&K,NB18 Left eye YES 3 EOG R&K, NB18 Right eye YES 4 EMG R&K, NB18 submentYES YES YES YES 5 EMG R&K, NB18 selectEMG YES YES YES 6 EEG BIC, NB18Fp1 YES YES 7 EEG BIC, NB1, 18 Fp2 YES YES 8 EEG BIC, NB1, 18 Fpz YES 9EEG AEP Mastoid+ YES 10 EEG AEP mid-foreh− YES 11 EMG EP L-EP+ YES 12EMG EP L-EP− YES 13 EYE TRK EYE-LID + YES 14 EYE TRK EYE-LID − YES 15ECG Vital-Signs 16 SAO2-HR Vital-Signs 17 SAO2 Vital-Signs 18 SAO2-PTTVital-Signs 19 BloodPres Vital-Signs

Block 9 (FIG. 18) Analysis Summary Data

Analysis Summary Data INSERT INDEPTHANESTH, BLOCK 9 Signal AnalysisValidity Priority User Valid or (Reference: Analysis Input InvalidAnalysis Analysis Algorithm Algorithm Signal Electrode Select SeeFormat& Interface Type & Algorithm Period Signal Group Placement On/OffSigVal Priority Version Version Period Type Channel Type Type NB1 NB 1NB 3 NB 4 NB 5 NB 6 NB 7 NB 8 SIGNAL CONFIGURATION AND TABLE REFERENCES1 EEG R&K, NB1, 18 C3 2 EOG R&K, NB18 Left eye 3 EOG R&K, NB18 Right eye4 EMG R&K, NB18 subment 5 EMG R&K, NB18 selectEMG 6 EEG BIC, NB18 Fp1 7EEG BIC, NB1, 18 Fp2 8 EEG BIC, NB1, 18 Fpz 9 EEG AEP Mastoid+ 10 EEGAEP mid-foreh− 11 EMG EP L-EP+ 12 EMG EP L-EP− 13 EYE TRK EYE-LID + NA14 EYE TRK EYE-LID − NA 15 ECG Vital-Signs NA 16 SAO2-HR Vital-Signs 17SAO2 Vital-Signs 18 SAO2-PTT Vital-Signs 19 BloodPres Vital-Signs NAAnalysis Analysis Analysis Inputs, Analysis Analysis Analysis AnalysisReference Validity Outputs, Index Index Calibrate Patient AnalysisWeight Weighted Analysis Signal Conditions Unit Measure Reference DataState table Value Depth Channel Type NB 9 NB 10 NB 11 NB 12 NB 13 NB 14NB 15 NB 16 NB 17 SIGNAL CONFIGURATION AND TABLE REFERENCES 1 EEG 2 EOG3 EOG 4 EMG 5 EMG 6 EEG 7 EEG 8 EEG 9 EEG 10 EEG 11 EMG 12 EMG 13 EYETRK 14 EYE TRK 15 ECG 16 SAO2-HR 17 SAO2 18 SAO2-PTT 19 BloodPres

NB 1

These channels can be referenced for 95% edge analysis and/or ½ periodamplitude analysis for purpose of validating neurological hypnosis, wakeor sleep state. The following table is set-up in system configurationoptions.

NB 2

User select on/off—user can configure which input channels are selected

NB 3

Signal validity (valid or invalid) Signal validity table determineswhether the signal status is valid or invalid. Analysis formatValidation Flow Diagram shows an example of how the selected channelsand processing format.

NB 4

Analysis priority is determined by combination of input signals andvalidity of input signals. See diagram: Analysis format Validation FlowDiagram, which details example low diagrams detailing selection ofappropriate analysis, subject to input signal type and signalvalidation.

NB 5

Analysis Interface version is necessary to ensue that the analysis typeand version is compatible with analysis algorithm interface.

NB 6

Analysis algorithm type and version. Each analysis algorithm isinterfaced to main program by way of a standard analysis interface,which can be in the form of a DLL or other defined and standardinterface method. This function provides a means of configuring,updating and convenient definition and display of a system's analysisstatus and configuration.

NB 7 Algorithm Period

1 sec10 secs30 secs1 min2 min5 min10 min20 min30 min40 min60 min

NB 8 Algorithm Period Type Options

Average over past periodRunning Average over periodRunning Average since start

NB 9

Analysis inputs, outputs and conditions describe standard variablesassociated with interface between analysis algorithms and main programanalysis interface.

NB 10

Analysis index units refer to measure associated with Index, such asrespiratory events per hour for RDI.

NB 11

Analysis index measure refers to name of specific index—example is RDIor Respiratory Disturbance Index.

NB 12

Analysis calibrate reference refers to calibration data which wascompiled from measurements associated with a specific patient. This datacould be, for example, normal wake and/or sleep EEG bi-coherencereference data measured as part of a preparatory study to assist moreaccurate depth of anaesthesia monitoring during a patient's operation.

NB 13

Analysis patient data refers to special patient data such as Body MassIndex (BMI), patient age and patient sex, which can affect the amount ofanaesthetic drug required for a particular patient.

NB 14

Analysis state refers to the state of analysis such as wake, sleep,conscious or unconscious.

NB 15

Analysis reference weight table refers to specific table referenced forpurpose of allocating correct analysis weighted value.

NB 16

Analysis weighted value refers to value assigned for current analysisoutput

NB 17

Analysis Depth refers to the degree or depth of the analysis, where 1represents conscious or wake state and 10 represent greatest depth ofunconsciousness. In other words we could have an analysis depth of say 8(see NB 17) for BIC analysis state and weighted value (see NB 16) of 7(for example only). In this example the weighted value is determined bythe signal validity associated with—

a) Signal quality associated with BIC signalsb) Analysis priority associated with BIC signalsc) Analysis probability and consolidation

NB18

Arousal detection can also be detected from this channel by way offrequency shift detection.

FIG. 20A shows a flow diagram of computation of bicoherence, real tripleproduct and bispectral index in Block 10 of FIG. 18.

Computation of Bispectrum (B), Bicoherence and Real Triple Product

${B\left( {f\; 1f\; 2} \right)} = {{\sum\limits_{l = 1}^{L}{{{Xi}\left( {f\; 1} \right)}{{Xi}\left( {f\; 2} \right)}{{Xi}^{*}\left( {{fi} + {f\; 2}} \right)}}}}$

Epoch length=30 seconds75% overlap of epochs to reduce variance of bi-spectral estimateL=epochs, i.e. 1 minute of dataf1&f2 are frequency components in the FFT such that f1+f2≦fs/2 where fsis the sampling frequency

Real Triple Product (RTP)

${{RTP}\; \left( {}^{*}{f\; 1f\; 2} \right)} = {\sum\limits_{l = 1}^{L}{{{Pi}\left( {f\; 1} \right)}{{Pi}\left( {f\; 2} \right)}{{Pi}\left( {{f\; 1} + {f\; 2}} \right)}}}$

Where Pi(f1) is the Power Spectrum

P(F)=|X(F)|²

Bi-Coherence (BIC)

${{BIC}\left( {f\; 1f\; 2} \right)} = \frac{100{B\left( {f\; 1f\; 2} \right)}}{\left. \sqrt{}{{RTP}\left( {f\; 1f\; 2} \right)} \right.}$

ranging from 0 to 100%

FIG. 20B shows a graphical representation of bispectrum, bicoherence andreal triple product in Block 10 of FIG. 18.

Block 11—FIG. 18 Audio Evoked Potential Depth of Hypnosis FrequencySensitivity Analysis

FIG. 21A shows waveform trace1 representing a sample of frequency sweepsignals which can are applied to one or both of a patient's ears.

FIG. 21B shows waveform trace 2 representing the frequency sweep signalat a sensitivity lower than trace 1. FIG. 21C shows one form of hardwarefor generating the signals shown in FIGS. 21A and 21B. FIG. 21D showsone form of hardware for collecting AEP sensory data from a subject.

FIG. 21E shows Waveform Trace 3 representing a sample of the signalresulting from monitoring the ear sensory nerve when the patient's earis receiving signals such as trace 1 or trace 2. The system has thecapability of applying a range of frequencies at various sensitivitylevels to provide a gauge of the patient's response to frequency andsensitivity variations whilst undergoing anaesthesia. In this mannerrelatively complex audio performance evaluation of a patient ispossible. Detailed and precise performance evaluation assists inobtaining an accurate measure of critical thresholds (as determined byempirical clinical data for varying patient ages and types). Furthermoremore accurate determination is possible by calibrating the systemdetection (consciousness and unconsciousness) thresholds for a specificpatient. This may be achieved by measuring normal consciousness valuesand in some circumstances values as the subject transitions into sleep.

FIGS. 21F and 21G show graph 1 and graph 2 respectively representingexamples of AEP output results from measuring a sequence of input signalamplitudes at selected sensitivities for a range of frequency sweeps. Byoutputting the same sequence of frequency sweeps but with varyingsensitivities (eg. trace 1 and trace 2) it is possible to graph theeffect of the subjects hearing during anaesthesia and provide anaccurate assessment based on deterioration of frequency response andsensitivity of the Audio Evoked Potential signal, the likely criticalpoints in the process of anaesthesia (ie. the points where the patientis at low risk of audio-recall while undergoing operation procedure).

The above provides an extremely sensitive performance evaluation systemfor ear-related operations where monitoring of a patient's audio sensorynerve function can be critical. The same system may also be applied tocomprehensive measurement and evaluation of audio performance.

FIG. 21H shows graph 3 demonstrating a sample of varying response curvesexpected from the AEP electrode output when outputting to a patient aseries of frequencies at different sensitivities.

The same type of graphical curves are stored as part of the referenceblock to determine various stages of a subjects monitoredanaesthesia—ie. thresholds for a patient in consciousness andunconsciousness with low risk of audio recall.

Block 15—FIG. 18 System Output Alarms, Indicators and Displays CombinedConsciousness-Transition New Index Weighted Analysis

Display Level 1 Analysis Probability Analysis Weight Factor (1-10) Value10-max Context Analysis Type Context Analysis Method Context AnalysisMethod Consciousness (anaes depth) Spectral & ½ period 7 Spectral & ½period 10 Consciousness (anaes depth) R&K 5 R&K 8 Sleep/WakeBi-coherence 6 Bi-coherence 7 Transition Analysis Type TransitionAnalysis Method Transition Analysis Method Alertness AEP 10 AEP 9Movement Response Type Movement Analysis Method Movement Analysis MethodEye Lid Eye Lid 8 Eye Lid 6 Local Evoked Potential Local EvokedPotential 7 Local Evoked Potential 7 Arousal Arousal 5 Arousal 6 VitalSigns ECG HR 60 SAO2 76 Blood Pressure 305

FIG. 22A shows a bar graph of Context Analysis Method and FIG. 22 ashows the corresponding display validation status. Validation status isrepresented by a colour coded bar display wherein green indicates thatthe parameter is operating in an optimal area, orange indicates that itis operating in a marginal area outside the optimal area and redindicates that the parameter is operating in an invalid or unreliablearea.

FIG. 22B shows a bar graph of Context Analysis Probability and FIG. 22 bshows the corresponding display validation status. FIG. 22C shows a bargraph of Transition Analysis Method and FIG. 22 c shows thecorresponding display validation status. FIG. 22D shows a bar graph ofTransition Analysis Probability and FIG. 22 d shows the correspondingdisplay validation status. FIG. 22E shows a bar graph of MovementAnalysis Method and FIG. 22 e shows the corresponding display validationstatus. FIG. 22F shows a bar graph of Movement Analysis Probability andFIG. 22 f shows the corresponding display validation status.

Block 15—FIG. 18 System Output Alarms, Indicators and Displays

Consciousness index (Derivation of BIC).Transition Index (derivation of AEP and Arousal Index), withcross-linked verification and feedback (transition state holdingprecedent and over-riding priority over BIC derived Index).

FIGS. 23A to 23C show graphical representations of system output alarms,indicators and displays associated with Block 15 in FIG. 18. FIG. 23Ashows a typical AEP and BIC display and report output together with anintegrated and weighted example display of auto track AEP-BIC indexwherein the colour of the display indicates its value as set forth inthe figure. FIG. 23B shows a bar graph display of discrete sensory indexwherein the colour of the display indicates its validation status as setforth in the figure. FIG. 23C shows a sample display screen associatedwith a hospital in depth anaesthesia meter/hospital ward rest meter withdepth anaesthesia analysis embodiment.

Block 16—FIG. 18 Arousal Detection

FIG. 24 shows a flow diagram of arousal detection in Block 16 of FIG.18.

Block 17—FIG. 18 Determination of Eyeopen Index (EOI)

Eye Opening Sensor Device (EOSD) outputs a unique voltage level inresponse to each eye opening status. The Actual Eye Opening Value (AEOV)is determined by detecting periods from the subject's consecutive blinksand detecting a maximum value of eye opening during these periods. Thisprocedure excludes blinks and effects of blinks, but rather extracts amaximum eye opening during the period.

The Reference Eye Opening Wake Value (REOWV) can be determined byinstigating the systems REOWV calibration procedure. This procedurerecords the Actual Eye Opening Value (AEOV) during a designated period,say for example 60 seconds, and then determines the average AEOV duringthis 60-second period.

${REOWV} = \frac{{total}\mspace{14mu} {addition}\mspace{14mu} {of}\mspace{14mu} {AEOV}\mspace{14mu} {for}\mspace{14mu} {calibration}\mspace{14mu} {time}\text{-}\left( {60\mspace{14mu} {seconds}} \right)}{{total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {AEOV}^{\prime}s\mspace{14mu} {for}\mspace{14mu} {calibration}\mspace{14mu} {time}\mspace{11mu} {period}}$

The Percentage Eye Opening (PEO) value can be calculated by dividing theActual Eye Opening Value (AEOV) by the Reference Eye Opening Wake Value(REOWV) and multiplying this value by 100 in order to determine the PEOvalue.

PEO=(AEOV/REOWV)×100

Eye Opening Index (PEOI) is calculated with the following formula

$\frac{100}{1} \times \frac{{Total}\mspace{14mu} {addition}\mspace{14mu} {of}\mspace{14mu} {AEOV}\mspace{14mu} {for}\mspace{14mu} 1\text{-}{minute}\mspace{14mu} {period}}{{Total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {AEOV}\mspace{14mu} {during}\mspace{14mu} {said}\mspace{14mu} 1\text{-}{minute}\mspace{14mu} {period}}$

PEOI=Percentage Eye Opening Index AEOV=The Actual Eye Opening ValueREOWV=Reference Eye Opening Wake Value AEOV=Actual Eye Opening ValuePEO=Percentage Eye Opening Determination of Eye Movement Index

The Eye Movement Index (EMI) is determined by detecting each Eye

Movement and using a running average formula determining the EMI for thepast time period t;

EMI=Total number of EM for period 1 minute (last minute for runningaverage calculation)wherein:t=time period under measurement—this is typically running time windowand for the EMI can be typically 1 minute (ie. representing EM's overthe past 1 minute for EMI running average)EM=Eye Movements. The eye movements are detected by way of output fromEOSD sensor and detecting for minimum period and threshold values

EMI=Eye Movement Index Block 21—FIG. 18 Sleep—Wake Analysis

Block 21 performs automatic recognition of sleep and wake states.

FIG. 25 shows a flow diagram of the process of detecting zero derivativetime instants and elementary maximum segments—1 in Block 21 of FIG. 18.

FIG. 26 shows a flow diagram of the process of detecting zero derivativetime instants and elementary minimum segments—1 in Block 21 of FIG. 18.

Block 21—FIG. 18 Sleep-Wake Analysis and BIC EEG Artifact Removal

FIG. 27 shows a flow diagram of the process of sleep/wake analysis andBIC EEG artefact removal in Block 21 of FIG. 18;

Blocks 23, 24, 25, 26, 27, 42, 29, 30, 31, 32, 33, 43 Display RangeScaling and Samples of Display Output (Block 15) Display Range Scaling

Display scaling is designed to provide the system end-user with a simpleand intuitive view of important analysis data whilst monitoring apatient.

A Display Range and Display Translation table has been designed fromempirical data (derived from clinical studies) to convert actualanalysis data values to normalised or weighted Display Unit Values(DUV). Accordingly, the Display Translation table can distort or providea non-linear translation across various sections of display translation,to improve visual tracking across critical regions of analysis data.

The Display Unit Values (DUV) are formulated to provide the system userwith a means to display the critical working range of each measuredvariable in a convenient and user friendly manner.

Furthermore, one or more Display Range Translation Tables (DRTT) may bedynamically allocated to a single Display Unit. The specific DRTTapplied at any point in time to a DU can be determined by the context ofor change sequence associated with a subject's hypnotic state. In thismanner typically different slopes or rates of change associated with asubjects measured variables may be displayed to present a maximised andAuditory Evoked Potential critical transition of a subject's state fromconsciousness to unconsciousness and visa versa.

Calculation of Display Scale Range Calculation

${DVD} = {\frac{{AV} - {MNS}}{SR} \times 100\mspace{14mu} \left( {{FULL}\mspace{14mu} {SCALE}\mspace{14mu} {RANGE}} \right)}$

Variables: Display Range Transition Table (DRTT) DSV=Display Screen View

DU=Display Unit which is one meter or trace that forms part of theDisplay Screen ViewMXS=maximum Scale Value is the Actual input data minimum or “cut-off”lower value displayedMNS=minimum Scale Value is the Actual input data maximum or “cut-off”higher value displayed

DVD=Display Value Deflection SRAD=MXS−MNS

AV=Actual Value, or the value that is currently being displayed by a DU.DR=Display Range. This can be, for example any value between 1 to 100.

DUV=Display Unit Values OWR=Optimal Working Range BIC Function and AEPTypical Values

Example of Display Range Translation tables for BIC function and AEPIndexes (note this is an example presenting 10 data points translationbut a complete table would present at least 100 data points).

Display Transition Step 1. Define Critical Zones of Display

The critical zone of display represents the values, which are desired tobe displayed in such a manner that the user has an expanded viewingrange (on meter display, for example) compared to less critical displayzones. In the HCM system the ability exists to define these “criticaldisplay zones” and in particular the critical display zones can changesubject to both the context of a subjects current and past states ofconscious/wake or unconscious/sleep.

Step 2.

Define critical threshold values.

These values are typically the following data points.

The following table defines the default critical values. These defaultvalues can be changed or modified in accordance with the user interfaceor different system configuration requirements.

Display Critical Threshold Value and Display Transition for BIC Analysis(DCTT)

TABLE DCTT BIC\CTUT BIC\UCTCT BIC critical BIC critical Displaytransition Display transition BIC DATA CTUT Thresholds UCTCT ThresholdsFactor-Negative Factor-Positive RANGE Negative Slope Positive Slopeslope slope COLUMN 1 COLUMN 2 COLUMN 3 COLUMN 4 COLUMN 5  0-10 .5 111-20 .5 1 21-30 .5 1 31-40 DCTTW Threshold-35 DCTTW Threshold-35 2 141-50 2 1 51-60 2 1 61-70 CTUT Threshold-80 2 3 71-80 UTCT Threshold-752 3 81-90 2 3 91-100 2 3 Translation Weighted BIC function Display 1-100value as (weighted in accordance normalised values. derived from withCTUT and UCTCT (divide by 300/100 columns 4 translation values. androunded to and 5 above. (per column 4 & 5) nearest whole unit) COLUMN 6COLUMN 7 COLUMN 8 COLUMN 9 T1- 85-(pos slope) 3 255 75 T2- 90-(posslope) 3 270 90 T3- 42-(pos slope) 1 42 14 T4- 41-(neg slope) 2 82 27T5- 38-(neg slope) 2 76 25 T6- 52-(pos slope) 1 52 17 T7- 62-(pos slope)3 186 62 T8- 71-(pos slope) 3 213 71 T9- 75-(pos slope) 3 225 75 T10-80(pos slope) 3 240 80

Display Critical Threshold Value and Display Transition for AEP Analysis

AEP critical AEP critical AEP\CTUT AEP\UCTCT CTUT Thresholds UCTCTThresholds Display transition Display transition AEP DATA Negative Slope+/− 10% Positive Slope +/− 10% Factor-Negative Factor-Positive RANGE-see block 39¹ -see block 39¹ slope slope COLUMN 1 COLUMN 2 COLUMN 3COLUMN 4 COLUMN 5  0-10 1 1 11-20 1 1 21-30 DCTTW Threshold-25 DCTTWThreshold-25 1 1 31-40 1 1 41-50 UTCT Threshold-50 2 2 51-60 2 2 61-70CTUT Threshold-65 2 2 71-80 2 1 81-90 1 1 91-100 1 1 TranslationWeighted AEP values Display 1-100 value as (weighted in accordancenormalised values. Typical AEP derived from with CTUT and UCTCT (divideby 160/100 values for time columns 4 translation values. and rounded tosequence T1 to T10 and 5 above. (per column 4 & 5) nearest whole unit)COLUMN 6 COLUMN 7 COLUMN 8 COLUMN 9 T1-77-(neg slope) 2 154 96 T2-76-(neg slope) 2 152 95 T3- 37-(neg slope) 1 37 23 T4- 35-(neg slope) 135 22 T5- 36-(pos slope) 1 36 23 T6- 40-(pos slope) 1 40 25 T7- 39-(negslope) 1 39 24 T8- 38-(neg slope) 1 38 24 T9-60- (pos slope) 2 120 75T10-75 (pos slope) 1 75 47 Note 1- -refer block 39 for details onselector logic for BIC and AEP combined output.

FIG. 28 shows weighted and display normalized (1-00) BIC and AEP data.

Above Example with combined BIC and AEP Display (refer blocks 12, 14 and34)

Note that switching between BIC and AEP is in accordance with Block 12logic or;

-   1. Consciousness (wake) to unconsciousness (sleep) state    transition—switch to BIC function-   2. Unconsciousness (sleep) to consciousness (wake) state    transition—switch to AEP value-   3. During consciousness (wake) state—switch to AEP value-   4. During unconsciousness (sleep) state—switch to BIC function

State Conscious Unconscious Weighted, Normalised (1- Wake* sleep*(example Weighted and 100) and combined BIC indicates only Normalised(1- Weighted and function and AEP Value consciousness and 100) BICNormalised (1- Greater of values Time period unconsciousness state).function 100) AEP Value from columns 3 and 4. COLUMN 1 COLUMN 2 COLUMN 3COLUMN 4 COLUMN 5 T1 consciousness 75 96 96 T2 consciousness 90 95 95 T3unconsciousness 14 23 23 T4 unconsciousness 27 22 27 T5 unconsciousness25 23 25 T6 unconsciousness 17 25 25 T7 unconsciousness 62 24 62 T8unconsciousness 71 24 71 T9 consciousness 75 75 75 T10 consciousness 8047 80

Sleep and wake states can include stage 1 sleep, stage 2 sleep, stage 3sleep, stage 4 sleep, REM sleep, movement sleep, arousal sleep andmicro-arousal sleep subject to the HCM system's applicationconfiguration and user's required sensitivity (ie. system may beconfigured and selected for application as a sedation or activitymonitor for the aged or subjects undergoing drug administration, inwhich cases the HCM system may be configured and selected for full-sleepstate sensitivity. Alternatively the HCM system may be selected forvigilance monitoring with a jet pilot or other transport driver or pilotsteering a ship or other sea vehicle and in this case electrodeattachments to the subject may be as minimal as a disposable wirelesslinked electrode for BIC parameters. Accordingly only level and state ofconscious or unconsciousness may be required.

Sample of Combined AEP and BIC with Critical Threshold and Patient StateDisplay

FIG. 29 is a sample of combined and weighted BIC and AEP data withcritical threshold and patient state display.

Status and Critical Threshold Display—Last 10, 20, 30, 40, 50, 60, 70;80, 90, or 100 Epochs of 30 Seconds (Subject to User Requirements andApplication)

-   -   sample for above t1 to t10 period with basic main states.    -   The data appearing to roll down in the format provides users a        clear graphic means of detecting the monitored subject's        progression of consciousness states, consciousness transitions        and critical thresholds.

TIME EPOCH # CONSCIOUS CTUT UNCONSCIOUS UTCT DCTTW 10:44:16 300 10:44:00299 10:43:30 298 10:43:00 297 10:42:30 296 10:42:00 295 10:31:30 29410:31:00 293 10:30:30 292 10:30:00 291

Sample for above t1 to t10 period can include basic main states andsleep states (would be the same as above table with the inclusion ofstates wake, stage 1 sleep, stage 2 sleep, stage 3 sleep, stage 4 sleep,REM sleep, movement sleep, arousal sleep and micro-arousal sleep).

Key; CTUT—Consciousness To Unconsciousness TransitionUTCT—Unconsciousness To Consciousness Transition DCTTW—DeepConsciousness Transition Threshold Warning

CS—Conscious state

US—Unconscious State

*Slope indicates that value is measured in conjunction with increasing(positive slope) or decreasing (negative slope).

Step 3

Define the transition formula associated with each segment or section ofthe display. Transition formula refers to a single co-efficient (such as0.5 or 2, for example) for the formula such as log of input value. Thistransition formula defines the method whereby different display sectionsare amplified, divided, distorted, stretched. For a viewing perspectivethe display may be contracted or expanded. The display transition may beimportant to simplify verification of the subjects status, i.e. Index ofBIC or Index of AEP or Arousal Index. Using the application of displaytransition method, the HCM system presents to the user a clear andconcise operation method whereupon each compliance or optimal status ofeach parameter can be quickly and easily verified by ensuring that themetered level falls within the optimal display range. Furthermore, eachcritical parameter being measured (such as Hypnosis Sensory-BIC Index,Auditory Sensory-AEP Index, Muscle Sensory-Arousal Index, VisualSensory-Eye Opening Index, Eye Movement Sensory-Eye Movement Index-EOI)may be viewed across a common optimal working and the display graphs canbe colour coded so that the user is given colour and positionalinformation which instantly verifies whether or not the subject'sphysiological parameters are measured in the optimal zone or displayarea at any point in time. With dangerous and critical drugadministration the ability to monitor a number of critical variableswith simple and accurate verification can avert an otherwise fatal orcritical situation for the subject under monitoring. For example, thesystem user may be instructed to administer the anaesthesia drug whileensuring that each sensory graph such as Hypnosis Sensory-BIC Index,Auditory Sensory-AEP Index, Muscle Sensory-Arousal Index, VisualSensory-Eye Opening Index, Eye Movement Sensory-Eye Movement Index-EOIor an Integrated Sensory Index (combined discrete sensory Indexes) arewithin the optimal range (colour and position) during drugadministration.

In particular the current method may provide users a simple and precisemethod of metering critical variables being analysed for a subjectundergoing administration of potentially dangerous drugs such as drugspromoting anaesthesia.

BIC and AEP Index Typical Unweighted Data

FIG. 23A shows typical AEP and BIC index display together with anintegrated and weighted example display of auto track AEP-BIC index.

BIC and AEP Index Typical Weighted Data with Expansion of CriticalDisplay Regions

-   -   FIG. 23B shows a discrete sensory index display example        including:    -   HYPNOSIS (45)    -   AUDITORY (78)    -   MUSCLE (44)    -   EYE MOVE (76)    -   EYE OPEN (50)    -   Integrated and Weighted Sensory Example

Step 4

Verify or modify Display Translation coefficients or critical thresholdsusing empirical data derived from clinical studies.

Block 29 FIG. 18 CSCA Data Translation Table (DTT) & Alarm Thresholds(AT) & Level Normalisation (LN)

The translation tables provide a means to translate raw analysis outputdata into a non-linear or linear manner. The translated data is outputin a form suitable for user display viewing. The working or optimalvalue range for various analysis functions can be transposed in order tofit the screen display and resolution for ease of user system operation.

Important or critical threshold values, associated with analysis dataoutput provide a means for the system to automatically generate alarmindicators or displays. For example, transition from conscious tounconscious and transition from unconscious are critical thresholds,which would be displayed as critical status displays.

Block 35—FIG. 18 Weighting for Combined (1, 2, 3, 4, 5) Index

The analysis index from 1) CORTICAL SENSORY (EEG) CONSCIOUSNESSANALYSIS, 2) AUDITORY SENSORY TRANSITION ANALYSIS (ASTA) AEP, 3) MUSCLESENSORY AROUSAL ANALYSIS, 4) VISUAL SENSORY ANALYSIS, 5) SLEEP/WAKESENSORY ANALYSIS input and combined with a formula to provide a singleindex designed to register the maximal value of 1, 2, 3, 4 and 5 at anypoint in time;

Select output value to maximal value from 1, 2, 3, 4 and 5 inputs.

FIGS. 30A and 30B show tables of examples of weighting for combined (1,2, 3, 4, 5) analysis index in Block 35 of FIG. 18.

Block 37—FIG. 18 Transition State Analysis

BODY MOVEMENT, (34), AROUSAL (35), AEP (30) ANALYSIS ALGORITHMS.

Block 37 Context & Transition Weighting Analysis

FIG. 31 shows an example format for transition weighting based uponcontext analysis in Block 37 of FIG. 18.

Weighting based upon context analysis, BIC co-efficient table (range ofBIC function versus critical thresholds, and weighting value versus BICfunction).

Consciousness Probability

-   -   Compute Bi-spectrum    -   Real-Triple Product    -   Bi-coherence

Transition State

-   -   AEP    -   Arousal    -   Eye movement Analysis    -   EOG analysis    -   EMG analysis (Chin)

Combined AEP and BIC Index for Consciousness & UnconsciousnessDetermination Using BIC and R&K in Unique Decision Context

FIG. 32 shows a flow diagram for determiningconsciousness/unconsciousness using combined AEP and BIC index and R & Kin decision context in Block 37 of FIG. 18.

Block 44

-   -   GSR (galvanic skin response) or EDA (electrodermal activity) or        SCR (skin conductivity response)

GSR (galvanic skin response) or EDA (electro dermal activity) or SCR(skin conductivity response) as it is now called, is a measure of theconductivity of the skin from the fingers and/or palms. In practice themeasurement is made by passing a constant current through the electrodesto determine the skin resistance.

Physiologically the EDA is a measure of sweat gland activity. Increasedsympathetic nervous activity will cause sweat to be released onto thepalms, thus increasing the conductance. Many emotions such as fear,anger and being startled will elicit increased sympatheticactivity—hence its use in lie-detectors and biofeedback relaxationtraining.

Block 44-51—FIG. 18 Stress and Anxiety Analysis

The HCM system proposes to apply periodic cuff attached (arm, wrist orother patient attachment location) blood-pressure measurement system, inconjunction with an oximeter pulse waveform and ECG waveform (for PTTcalculation). The method of utilising the PTT (by way of oximeter pulsewave and ECG waveform) together with periodic cuff based blood-pressuremeasurement provides a means to derive the quantitative blood-pressuremeasurement from the cuff value, and the qualitative blood-pressuremeasurement from the PTT calculated signal. In other words the baselinequantitative blood-pressure value is derived from the cuffblood-pressure value, while continuous and qualitative blood pressurevalue is derived from the PTT value. Furthermore the application of PAT(104-108) measurement as a means of sensitive EEG arousal detectionpotentially provides a new method for minimally invasive and maximallysensitive arousals detection. In the context of monitoring a subject ina minimally invasive fashion, and with the intent of reducing the riskassociated with premature awakening during an anaesthesia relatedprocedure this new method provides promising scope and application. Thebenefit of this type of system is its accuracy and continuous bloodpressure monitoring capability, while maintaining patient comfort byonly implementing the cuff inflation and deflation at periodic timeintervals.

Furthermore the system has a capability to simplify user operation withapplication of wireless interconnection of the pulse oximeter, ECGelectrode and blood pressure cuff. This wireless interconnection mayallow calculation of continuous blood pressure at a remote wireless orwire-linked site (such as a patient monitoring device), at the EFCGelectrode attachment site, at the oximeter finer probe site or the bloodpressure cuff site (refer FIG. 33).

Respiration and in-Depth Anaesthesia Monitoring

Effects of paced respiration and expectations on physiological andphysiological responses to threat, anxiety or stress conditions can bedetected by monitoring a subject's respiration rate.

These states of threat, anxiety, or stress may be expected in a casewhere a patient partially or fully awakens during a medical procedure.In many cases muscles are paralyzed through special muscle relaxants,and the ability to alert surrounding people may be disabled.

Measurement of Respiration Rate and Respiration Rate Variability

Step 1. Determine the respiration rate for the past 60 second period.This is repeated after every second for the past 30 seconds ofrespiratory data to produce a running average respiratory ratevariability.

Step 2. A similar method as described in block 21 is applied to providea syntactic or breath-by-breath detection of the respiratory waveform.The respiratory waveform data can be derived (subject to systemconfiguration) from Respiratory Inductive Plethysmography or other typeof respiratory bands or patient airflow sensors. Alternatively therespiratory waveform can be derived indirectly from channels such asPTT, ECG, ECG, amongst others.

Step 3. An average baseline (AB) for the past 5 minutes (period isnominal but adjusted with reference to empirical clinical data) iscalculated as a mean average. The change of respiration (CR) for thepast 1 minute (period is nominal but adjusted with reference toempirical clinical data) is measured against the stated AB value, toproduce the current Respiration Variability Rate value (RVRV).

RVRV=CR/AB

RVRV is compared to threshold values (TV) alarm or notificationindication for user or user display. This notification can be in theform of color changes of screen display, meter threshold or the like.TV's are determined from empirical clinical data for the range of normalrespiration, anxious or high level respiration and below normalrespiration.

Step 4. The RVRV, AB, CR are available for display against the variousthreshold guide values (ie. TV's) (53).

Heart Rate and in-Depth Anaesthesia Monitoring

(see refs. 54, 55, 56, 57, 60)

Galvanic Skin Response

Galvanic Skin Response is one physiological parameter, which has beenfound to be associated with threatening or stressful conditions and maybe correlated with patients under stress. Galvanic Skin Response may beevident during premature waking associated with an anesthetic procedure.

Blood Pressure and in-Depth Anaesthesia Monitoring

FIG. 33 shows one form of apparatus for wireless linked continuous bloodpressure measurement (see ref. 58).

Improved Biological Sensor for Sensing and Measuring Eye Opening

FIG. 34A shows one form of biological sensor device for sensing andmeasuring eye opening. The biological sensor includes a pair of scissorarms 34, 35 connected for pivot able movement at hinge 36. Arm 34 isadapted to move substantially with an eyelid. In one form the free endof arm 34 may be fixed to a movable part of the eyelid by means of anadhesive such as double-sided tape. The free end of arm 35 may be fixedto part near the eye that substantially does not move with the eyelid.Each arm 35, 36 includes conductive carbon tracks 37. Tracks 37 may forman inductor on each arm. Alternatively tracks 37 may form a plate of acapacitor on each arm. It may be seen that as arms 34, 35 move or pivotrelative to each other the degree of over lap between carbon tracks 37on the respective arms changes with the movement. Tracks 37 areconnected to an Electronics Interface for converting the position ofarms 35,36 to an electrical signal.

FIG. 34B shows one form of Electronics Interface wherein the eye tracksensor is represented by a variable inductor 37A for tracking eyelidposition. Variable inductor 37A is formed with carbon tracks onrespective arms 34, 35. Variable inductor 37A includes a coil on eacharm 34, 35 arranged such that movement of the arms changes the amount ofcoupling between the coils and therefore the inductance value of eachcoil.

The inductance value may be measured in any suitable manner and by anysuitable means such as a wien bridge. In one form the inductance valuemay be measured by a circuit including oscillator 38, resistor 39 andlow pass filter 40. The output of low pass filter 40 provides a signalthat is indicative of the relative position of arms 34, 35 and henceprovides a measure of eye opening. An additional measure of eyelidactivity is provided via EOG electrodes 41, 42 at the free ends arms34,35. Electrodes 41, 42 are connected to suitable monitoring apparatusvia respective wires 43, 44.

FIG. 34C shows one form of Electronics Interface wherein the eye tracksensor is represented by a variable capacitor 37B for tracking eyelidposition. The embodiment shown in FIG. 34C is similar to the embodimentof FIG. 34B except that variable capacitor 37B is formed with carbontracks on the respective arms 34, 35. Variable capacitor 37B includes acapacitor plate on each arm separated by an insulator (dielectric) andis arranged such that movement of the arms changes the amount ofcoupling between the plates and therefore the capacitance value of thevariable capacitor. The capacitance value is measured by the circuitshown in FIG. 37C which is similar to the circuit in FIG. 34B.

Integrated Anaesthesia Monitoring Electrode System (IAMES) BlockDiagram—Wireless or Wired Version—refer FIG. 35

FIG. 35 shows one form of electrode system for integrated anaesthesiamonitoring. The IAMES system may be applied for each wireless electrodesset. 2 unique components may be utilised, including the ElectrodeAttachment System (EAS) and the Wireless Electronic System (WES).

FIG. 36 shows a sample embodiment of a wire connected sensor deviceincluding bi-coherence, EOG, chin EMG and Eye Opening.

Integrated Sleep Electrode System (ISES)—Refer FIG. 37

Sample of embodiment including bi-coherence, EOG, chin EMG and EyeOpening Wireless Sensor Device.

FIG. 37 shows a sample embodiment of a wireless integrated electrodesystem including bi-coherence, EOG chin EMG and Eye Opening.

The ISES system may be applied for each wireless electrodes set. 2unique components may be utilised, including the Electrode AttachmentSystem (EAS) and the Wireless Electronic System (WES).

Note—all above electrode positions may include an optional redundantelectrode system to allow automatic electrode switching or exchangewhere a poor quality or excessively high impedance electrode isdetected.

Wireless Electrode Preferred Embodiment (WEPE)—refer FIG. 38

FIG. 38 shows a preferred embodiment of a wireless electrode.

A radio transmitter sends data to a PC within the same room (operatingtheatre) which analyses EEG and determines depth of anaesthesia.

Transmitter Unit

Battery powered—Maxell rechargeable lithium cell. 3V 65 mAh 3 mm×20 mmdiameter ML2033.Should provide at least 12 hours operation from a single charge, ideally24 hours—so that it may be used for other applications.

Radio Transmitter

-   -   Prefer use 915 MHz ISM band or 2.4 GHz ISM band.    -   Prefer spread spectrum so that signal is less prone to        interference than a single carrier frequency.    -   Lower power average <65 mA/12.    -   Transmission range 10 m.    -   Data rate average 256×12=3000 bps min. ie. 256 samples per        second, 12 bits/sample prefer 16 bits/sample.    -   Would expect to have much higher Tx data rate but only use low        duty cycle to save power.    -   Prefer operate at 3V or less.    -   Blue tooth has too much protocol overhead to get really low        power consumption

Data Acquisition

Done by micro-controller, which also controls radio transmitter. Use 16bit or 12 bit with differential end—(INA122) or discrete op. amps.

Spread spectrum transmitters normally have receivers to convey hoppingsequence and/or that data has been correctly received.

Texas Instruments TRF6900 3 V single chip radio transceiver.Tx 21 mA @ 20 dB attenuation, 37 mA @ 0 dB attenuation.

Rx 24 mA

Power down 2 mA

Using MSP430 micro-controller to perform base band operations and dataacquisition.

A system with one master unit may collect acquisition data from up to 12slaves. Each slave may collect 512 bytes of data per second and transmitthis to the master. The whole system may operate at the LIPD (LowInterference Potential Device) ISM band at 915-928 MHz. This is an“unlicensed band” and is subject to the “no interference, no protection”policy. No protection implies that several methods have to be devised tomake the whole system as interference-immune as possible.

The main design criteria are listed below in order of importance.

-   -   Minimal current consumption in slave (ideally <2 mA).    -   Maximum immunity to interference.    -   Small physical size.    -   Component lead-time<8-12 weeks.    -   System manufacturing cost.

Channel Assignment

The ISM band is located between the GSM mobile and GSM base station bandat 915-928 MHz. Channel spacing is decided to be 500 kHz, giving 24usable channels for the frequency-hopping scheme.

Master Unit—Refer FIG. 39

Current consumption is not an adverse factor on the master unit, so themaster will have to control all RF traffic. In each 1-second time sliceup to 12 slave transactions of 512 bytes may be made.

Referring to FIG. 39, the following scheme is proposed:

At a data rate of approximately 110 kBps a 512 byte NRZ packet will take46.5 mS. Timeslots of approx. 70 mS are allocated for each slave,totalling 840 mS. The remaining 160 mS are arbitrary timeslots reservedfor retries on unsuccessful slave transfers (up to 2 for each second).

On power-up, the master starts “calling” for slaves using short formatpackets. During this acquisition process all channels are sequentiallyscanned to find free channels for each timeslot subsequently assigned toa slave. Each time a slave is found a “time marker” is set in the masterindicating which slave needs to be acquired on which channel in the nexttimeslot (1000 mS later). When a transfer from slave to master is due,the master first sends a synchronisation packet and waits for therelevant slave's acknowledge. If the slave does not reply, the masterstarts sending sync packets while hopping the channels based on a PNsequence seeded by the current targeted slave's ID. The slave itselfalso follows the same PN sequence. About 20 retries are allowed for so anew channel can be found for the slave to transfer its 512 byteacquisition packet in case of “jamming”.

Once data transfer with one or more slaves starts, a host PC willcollect the data via an RS232 interface, possibly incorporating RTS/CTSlines for hardware handshake. Since the MSP430F149 has 2 KB of RAM, itis expected that 1.5 KB will be reserved for the acquisition data, so a3 level deep “FIFO” can be implemented on the master. This may be usefulin case the host PC has say Windows calling it to perform otherfunctions. This implies that the PC host software can have a maximumlatency of 2 seconds to collect the data, otherwise an overrun willoccur.

Slave Unit—Refer FIG. 40

On power-up, the slave goes into receive mode waiting for a master syncpacket (Master Acquisition Mode—MAM).

A sync packet will approximately take 1.4 mS, including preamble, frameheader, descriptor and CRC (approx. 150 bits)

An arbitrary amount of time is designated for the slave to spend in MAM,say 10 seconds. If a master sync is not acquired, the slave waits for 20seconds and enters MAM again.

This is to avoid excessive current consumption in case the master is notpresent or fails during operation of the network. Once sync is achievedwith the master (Master Sync Mode—MSM), the slave starts taking 256 A/Dsamples with a resolution of 12-16 bits every second. These are storedin a RAM buffer and will be transferred to the master at the end of each1-second time slice.

The PCB for the slave is intended to be identical to the master's H/W.

The RS232 pads will be used to assign an ID to the slave and store it inFlash.

Slave Current Consumption

The Slave's current consumption is made up of 3 components, namely thecontinuous current, peak transceiver current component, and peak A/Dconversion component and works out to be approx. 1.82 mA. Each retry fora slave in a 1 second time slice incurs an extra 1.74 mA. This isanticipated to be unlikely since 24 channels are available at +5 dBmoutput.

Continuous Slave Current

The MSP430F149's LFXTAL is running with a 32.768 kHz crystal and clocksthe internal Timer A. This Timer has a three-channel Capture/CompareUnit and will be used to interrupt the core at a 256 Hz rate for A/Dconversion. This is the continuous component.

Transceiver Peak Current

In each 1-second time slice the TRF6900 will be active for about 50 mStotal. The sequence is as follows:

-   -   On wake-up, The XT2 oscillator is started and is allowed        start-up (Crystal oscillators typically will start from 5-10        mS), together with the DDS reference.    -   The CPU is now turned on and provides ample processor throughput        to handle the 110 kBps link and SPI communication with the        TRF6900 transceiver block. About 1 mS is needed to set-up the        TRF6900 into receive and lock.    -   The slave has CPU+TRF6900 activated for approx. 2 mS, assuming        good BER and clear channel.    -   The TRF6900 is put in TX mode. It is decided to initially output        the full output power on the TRF6900. This results in a higher        peak current but will ensure minimal BER and therefore retries        thereby minimising current.

A/D Conversion Peak Current

The A/D converter has its own RC internal clock and does a conversion inmax 4 uS. (12 bit resolution).

Software

The firmware will be written in “C” to allow for clarity and easyexpansion. It is worth noting that after production the design can beported to a MSP430F147 to reduce cost. Further expansion and additionshould be made easier by a considerable amount of spare program memory(the F149 has 60 KB Flash memory). The presence of a 1 cyclesigned/unsigned 16×16 into 32 bit H/W MAC will be useful for possiblefuture DSP additions like wave filtering.

Hardware

The Slave and Master PCB should be identical and will be implemented ona 4 layer PCB.

Analysis Overview—a Breakdown of Primary, Secondary and TertiaryAnalysis—Refer FIG. 41

Vehicle Bicoherence Wireless System (VBWS)—Refer FIG. 42—Car VigilanceSystem

System Hardware Block Diagram

The block diagram in FIG. 42 shows a system consisting of wirelessattached electrodes to patient's forehead and wireless interface forelectrode signal pick-up and EEG processing, within a drivingenvironment. EEG processing can include coherence spectral analysisand/or Audio Evoked Response.

The VBWS system can be applied for each wireless electrodes set. 2unique components can be utilised, including the Electrode AttachmentSystem (EAS) and the Wireless Electronic System (WES).

Audio Visual Flow Diagram (AVF)—Refer FIG. 43

FIG. 43 shows a sample embodiment using synchronized audio and video asa means for in-depth anesthesia system validation and recall apparatus.

The embodiment includes use of bi-phasic and AEP in-depth anaesthesiamonitoring system with synchronised video detection and recordingcapability.

Pain Level or Consciousness Level Remote Indicator (PLCLRI)—Refer FIG.44

Pain Level or Consciousness Level Remote Indicator

Spread Spectrum Based Wireless, Active Electrode System (SSBWAES)—ReferFIGS. 45 and 46

Spread Spectrum Based Wireless, Active Electrode System, with redundantelectrode substitution, dynamic signal quality verification, impedanceverification and calibration.

FIG. 45 shows a direct connected wireless module.

FIG. 46 shows an indirect connected wireless module.

The SSBWAES system may be applied for each wireless electrodes set. 2unique components may be utilised, including the Electrode AttachmentSystem (EAS) and the Wireless Electronic System (WES).

Example of embodiment of wireless based active electrode system.

FIG. 47 shows one embodiment of a wireless based active electrodesystem.

Biofeedback Controlled Drug Delivery System Linked to ConsciousnessMonitoring Investigational Device (BCDDSLCIG)—Refer FIG. 48

FIG. 48 shows a drug delivery system linked to aconsciousness-monitoring device.

Finally, it is to be understood that various alterations, modificationsand/or additions may be introduced into the constructions andarrangements of parts previously described without departing from thespirit or ambit of the invention.

APPENDIX I References Adjoining Patent Application

The HCM system utilises a range of different parameters, which allow theuser to establish a library or range of patient input variables, a rangeof different secondary analysis and a range of different weighting andsummary tertiary analysis as the means to determine the depth ofanaesthesia for a particular subject. The following studies demonstratethat the use of a simple or single dimension or measure for depth ofanaesthesia, while desirable, is not practical with such a complexphysiological state and change of state:

-   1.-   Barr G, Anderson R E, Samuelsson S, Owall A, Jakobsson J G,    described in British Journal of Anaesthesia June 2000, PMID:    10895750, UI: 20354305 “Fentanyl and midazolam anaesthesia for    coronary bypass surgery: a clinical study of bi-spectral    electroencephalogram analysis, drug concentrations and recall.” In    this study, Barr and colleagues describe: “Bi-spectral index (BIS)    was assessed as a monitor of depth of anaesthesia during fentanyl    and midazolam anaesthesia for coronary bypass surgery.” “BIS    decreased during anaesthesia, but varied considerably during surgery    (range 36-91) with eight patients having values>60. Midazolam and    fentanyl drug concentrations did not correlate with BIS. No patient    reported explicit or implicit recall. During clinically adequate    anaesthesia with midazolam and fentanyl BIS vanes considerably. The    most likely reason is that BIS is not an accurate measure of the    depth of anaesthesia when using this combination of agents.”-   2.-   Schraag S, Bothner U, Gajraj R, Kenny G N, Georgieff M, described in    Anesth Analg April 2000, PMID: 10553858, UI: 20019286 “The    performance of electroencephalogram bi-spectral index and auditory    evoked potential index to predict loss of consciousness during    propofol infusion.” In this study, Schraag and colleagues describe:    “The bi-spectral index (BIS) of the electroencephalogram and middle    latency auditory evoked potentials are likely candidates to measure    the level of unconsciousness and, thus, may improve the early    recovery profile.” “The electroencephalogram BIS and the auditory    evoked potential index (AEPi), a mathematical derivative of the    morphology of the auditory evoked potential waveform, were recorded    simultaneously in all patients during repeated transitions from    consciousness to unconsciousness.” “We conclude that both the BIS    and AEP are reliable means for monitoring the level of    unconsciousness during propofol infusion. However, AEPi proved to    offer more discriminatory power in the individual patient.    IMPLICATIONS: Both the bi-spectral index of the electroencephalogram    and the auditory evoked potentials index are good predictors of the    level of sedation and unconsciousness during propofol infusion.    However, the auditory evoked potentials index offers better    discriminatory power in describing the transition from the conscious    to the unconscious state in the individual patient.”-   3.-   Gajraj R J, Doi M, Mantzaridis H, Kenny G N, described in British    Journal of Anaesthesia May 1999, PMID: 10536541, UI: 20006623    “Comparison of bi-spectral EEG analysis and auditory evoked    potentials for monitoring depth of anaesthesia during propofol    anaesthesia.” In this study, Gajraj & colleagues describe: “We have    compared the auditory evoked potential index (AEP Index) and    bi-spectral index (BIS) for monitoring depth of anaesthesia in    spontaneously breathing surgical patients.” “The average awake    values of AEP Index were significantly higher than all average    values during unconsciousness but this was not the case for BIS. BIS    increased gradually during emergence from anaesthesia and may    therefore be able to predict recovery of consciousness at the end of    anaesthesia. AEPIndex was more able to detect the transition from    unconsciousness to consciousness.”-   4.-   Gajraj R J, Doi M, Mantzaridis H, Kenny G N, described in Br J    Anaesth January 1998, PMID: 9505777, UI: 98166676 “Analysis of the    EEG bispectrum, auditory evoked potentials and the EEG power    spectrum during repeated transitions from consciousness to    unconsciousness.” In this study, Gajraj & colleagues describe: “We    have compared the auditory evoked potential (AEP) index (a numerical    index derived from the AEP), 95% spectral edge frequency (SEF),    median frequency (MF) and the bi-spectral index (BIS) during    alternating periods of consciousness and unconsciousness produced by    target-controlled infusions of propofol.” “Our findings suggest that    of the four electrophysiological variables, AEP index was best at    distinguishing the transition from unconsciousness to    consciousness.”-   5.-   Witte H, Putsche P, Eiselt M, Hoffmann K, Schack B, Arnold M, Jager    H, described in: Neurosci Lett November 1997, PMID: 9406765, UI:    98068600 “Analysis of the interrelations between a low-frequency and    a high-frequency signal component in human neonatal EEG during quiet    sleep.” In this study, Witte and colleagues describe: “It can be    shown that dominant rhythmic signal components of neonatal EEG burst    patterns (discontinuous EEG in quiet sleep) are characterised by a    quadratic phase coupling (bi-spectral analysis). A so-called    ‘initial wave’ (narrow band rhythm within a frequency range of 3-12    Hz) can be demonstrated within the first part of the burst pattern.    The detection of this signal component and of the phase coupling is    more successful in the frontal region. By means of amplitude    demodulation of the ‘initial wave’ and a subsequent coherence    analysis the phase coupling can be attributed to an amplitude    modulation. i.e. the envelope curve of the ‘initial wave’ shows for    a distinct period of time the same qualitative course as the signal    trace of a ‘lower’ frequency component (0.75-3 Hz).”-   6.-   Schneider G, Sebel P S, described in Eur J Anaesthesiol Suppl May    1997, PMID: 9202934, UI: 97346517 “Monitoring depth of anaesthesia”.    In this study, Schneider & Sebel describe: “In clinical practice,    indirect and non-specific signs are used for monitoring anaesthetic    adequacy. These include haemodynamic, respiratory, muscular and    autonomic signs. These measures do not indicate adequacy of    anaesthesia in a reliable manner.” “EEG information can be reduced,    condensed and simplified, leading to single numbers (spectral edge    frequency and median frequency). These methods appear insufficient    for assessing anaesthetic adequacy. The bi-spectral index, derived    from bi-spectral analysis of the EEG, is a very promising tool for    measuring adequacy of anaesthesia. An alternative approach is to    monitor evoked potentials. Middle latency auditory evoked potentials    may be helpful in assessing anaesthetic adequacy. Both techniques    need further validation.”-   The following studies indicate the use of BIS as a strong indicator    of depth of anaesthesia and accordingly the HCM System utilises BIS    as one of the indices for in-depth anaesthesia but provides multiple    concurrent indices to ensure that the user is able to ultimately    provide an informed decision on the depth of a patient's anaesthesia    as opposed only the reliance of one indicator:-   7.-   Sandler N A, Sparks B S, described in J Oral Maxillofac Surg April    2000, PMID: 10759114, UI: 20220864 “The use of bi-spectral analysis    in patients undergoing intravenous sedation for third molar    extractions.” In this study, Sandler describes: “The Observer's    Assessment of Alertness and Sedation (OAA/S) scale was used to    subjectively assess the level of sedation observed by the    anaesthetist before initiating the sedation procedure and then at    5-minute intervals until the end of the procedure. The BIS level was    simultaneously recorded.” “The time and dose of the drug given were    recorded. The level of sedation based on a single anaesthetist's    interpretation (OAA/S) and the BIS readings were then compared.    RESULTS: A strong positive relationship between the BIS index and    OAA/S readings was found (P<0.0001).” “CONCLUSION: BIS technology    offers an objective, ordinal means of assessing the depth of    sedation. There was a strong relationship between the objective BIS    values and subjective assessment (OAA/S scale) of the depth of    anaesthesia. This can be invaluable in providing an objective    assessment of sedation in oral and maxillofacial surgery where it    may be difficult to determine the level of sedation clinically.”-   8.-   Denman W T, Swanson E L, Rosow D, Ezbicki K, Connors P D, Rosow C E,    described in Anesth Analg April 2000, PMID: 10735791, UI: 20200014    “Pediatric evaluation of the bi-spectral index (BIS) monitor and    correlation of BIS with end-tidal sevoflurane concentration in    infants and children.” In this study, Denman & colleagues describe:    “The bispectral index (BIS) has been developed in adults and    correlates well with clinical hypnotic effects of anesthetics. We    investigated whether BIS reflects clinical markers of hypnosis and    demonstrates agent dose-responsiveness in infants and children.”    “BIS correlated with clinical indicators of anesthesia in children    as it did in adults: as depth of anesthesia increased, BIS    diminished. BIS correlated with sevoflurane concentration in infants    and children.” The use of bispectral index (BIS) during general    anesthesia improves the titration of anesthetics in adults.”-   9.-   Hirota K, Matsunami K, Kudo T, Ishihara H, Matsuki A, described in    Eur J Anaesthesiol August 1999, PMID: 10500939, UI: 99430726    “Relation between bispectral index and plasma catecholamines after    oral diazepam premedication.” In this study, Hirota and colleagues    describe: “Venous blood samples (6 mL) were collected in the case of    patients in group D(+) for the measurement of plasma catecholamines    levels using high-performance liquid chromatography. The bispectral    index level (mean+/−SD) in group D(+): 93.5+/−773.5 was    significantly lower than that in group D(−): 96.1+/−1.8 (P<0.05).    There was a significant correlation between bispectral index and    plasma norepinephrine levels (r=0.567, P<0.05). study suggests that    the bispectral index monitor may detect the effect of oral diazepam    premedication.”-   10.-   Muthuswamy J, Roy R J, described in IEEE Trans Biomed Eng March    1999, PMID: 10097464, UI: 99197537 “The use of fuzzy integrals and    bispectral analysis of the electroencephalogram to predict movement    under anesthesia.” In this study, Muthuswamy and Roy describe: “The    objective of this study was to design and evaluate a methodology for    estimating the depth of anesthesia in a canine model that integrates    electroencephalogram (EEG)-derived autoregressive (AR) parameters,    hemodynamic parameters, and the alveolar anesthetic concentration.”    “Since the anesthetic dose versus depth of anesthesia curve is    highly nonlinear, a neural network (NN) was chosen as the basic    estimator and a multiple NN approach was conceived which took    hemodynamic parameters, EEG derived parameters, and anesthetic    concentration as input feature vectors. Since the estimation of the    depth of anesthesia involves cognitive as well as statistical    uncertainties, a fuzzy integral was used to integrate the individual    estimates of the various networks and to arrive at the final    estimate of the depth of anesthesia.” “The fuzzy integral of the    individual NN estimates (when tested on 43 feature vectors from    seven of the nine test experiments) classified 40 (93%) of them    correctly, offering a substantial improvement over the individual NN    estimates.”-   11-   Muthuswamy J, Sherman D L, Thakor N V, described in IEEE Trans    Biomed Eng January 1999, PMID: 9919830, UI: 99118483 “Higher-order    spectral analysis of burst patterns in EEG.” In this study,    Muthuswamy & Colleagues describe: “We study power spectral    parameters and bispectral parameters of the EEG at baseline, during    early recovery from an asphyxic arrest (EEG burst patterns) and    during late recovery after EEG evolves into a more continuous    activity. The bicoherence indexes, which indicate the degree of    phase coupling between two frequency components of a signal, are    significantly higher within the delta-theta band of the EEG bursts    than in the baseline or late recovery waveforms. The bispectral    parameters show a more detectable trend than the power spectral    parameters.” “The bicoherence indexes and the diagonal elements of    the polyspectrum strongly indicate the presence of nonlinearities of    order two and in many cases higher, in the EEG generator during    episodes of bursting. This indication of nonlinearity in EEG signals    provides a novel quantitative measure of brain's response to    injury.”-   12.-   Lipton J M, Dabke K P, Alison J F, Cheng H, Yates L, Brown T I,    described in: Australas Phys Eng Sci Med March 1998, PMID: 9633147,    UI: 98296803 “Use of the bispectrum to analyse properties of the    human electrocardiograph.” In this study, Lipton & colleagues    describe: “The bispectrum and bicoherence spectrum have been shown    to be powerful techniques for identifying different types of    nonlinear system responses. This paper presents an introduction to    bispectral techniques applied to biomedical signals and examines the    bispectral properties of the human electrocardiograph (ECG). The    bispectrum proves to be an effective tool for representing and    distinguishing different ECG response types. Bispectral ECG analysis    is non-invasive and may prove to be a useful discriminant    diagnostic.”-   13.-   Hall J D, Lockwood G G, described in Br J Anaesth March 1998, PMID:    9623435, UI: 98286638 “Bispectral index: comparison of two    montages.” In this study, Hall & Lockwood describe: “We have    compared fronto-central and bifrontal montages using a new EEG    monitor, the Aspect A-1000. The monitor uses bispectral analysis to    derive an index of anaesthetic depth, the bispectral index (BIS).”    “ECG electrodes placed in a bifrontal montage were more reliable    than silver dome electrodes in a fronto-central montage and both    types of electrodes had impedances in the clinically useful range.    However, BIS values derived from each montage were found to differ    in an unpredictable manner.” “We conclude that the BIS may be useful    for following trends in anaesthetic depth in individual cases but it    is less helpful when making comparison between patients or as a    single value.”-   14.-   Struys M, Versichelen L, Byttebier G, Mortier E, Moerman A, Rolly G,    described in Anaesthesia January 1998, PMID: 9505735, UI: 98166634    “Clinical usefulness of the bispectral index for titrating propofol    target effect-site concentration.” In this study, Struys and    colleagues describe: “A greater percentage of bispectral index    readings lying outside the target range (i.e. <40 or >60) and more    movement at incision and during maintenance were found in Group 1.    There was a trend towards more implicit awareness in patients in    Group 1.” “Propofol dosage could not be decreased but a more    consistent level of sedation could be maintained due to a more    satisfactory titration of target effect-site concentration.”-   15.-   Kearse L A Jr, Rosow C, Zaslaysky A, Connors P, Dershwitz M, Denman    W, described in Anaesthesia January 1998, PMID: 9447852, UI:    98107541 “Bispectral analysis of the electroencephalogram predicts    conscious processing of information during propofol sedation and    hypnosis.” In this study, Kearse & colleagues describe: “BACKGROUND:    The bispectral index (BIS) measures changes in the interfrequency    coupling of the electroencephalogram (EEG). The purposes of this    study were (1) to determine whether BIS correlates with responses to    command during sedation and hypnosis induced by propofol or propofol    and nitrous oxide, and (2) to compare BIS to targeted and measured    concentrations of propofol in predicting participants' responses to    commands.” “CONCLUSIONS: Bispectral index accurately predicts    response to verbal commands during sedation and hypnosis with    propofol or propofol plus nitrous oxide. Accuracy is maintained in    situations likely to be encountered during clinical use: when    propofol concentrations are increasing or decreasing and when    repeated measurements are made over time”.-   16.-   Glass P S, Bloom M, Kearse L, Rosow C, Sebel P, Manberg P, described    in Anesthesiology April 1997, PMID: 9105228, UI: 97259091    “Bispectral analysis measures sedation and memory effects of    propofol, midazolam, isoflurane, and alfentanil in healthy    volunteers.” In this study, Glass & colleagues describe: “At each    pseudo-steady-state drug concentration, a BIS score was recorded,    the participant was shown either a picture or given a word to    recall, an arterial blood sample was obtained for subsequent    analysis of drug concentration, and the participant was evaluated    for level of sedation as determined by the responsiveness portion of    the observer's assessment of the alertness/sedation scale (OAAS). An    OAAS score of 2 or less was considered unconscious. The BIS (version    2.5) score was recorded in real-time and the BIS (version 3.0) was    subsequently derived off-line from the recorded raw EEG data.”    “CONCLUSIONS: The BIS both correlated well with the level of    responsiveness and provided an excellent prediction of the loss of    consciousness. These results imply that BIS may be a valuable    monitor of the level of sedation and loss of consciousness for    propofol, midazolam, and isoflurane.”-   17.-   Sebel P S, Lang E, Rampil I J, White P F, Cork R, Jopling M, Smith N    T, Glass P S, Manberg P, described in Anesth Analg April 1997, PMID:    9085977, UI: 97240517 “A multicenter study of bispectral    electroencephalogram analysis for monitoring anesthetic effect.” In    this study, Sebel & colleagues describe: “Bispectral analysis (BIS)    of the electroencephalogram (EEG) has been shown in retrospective    studies to predict whether patients will move in response to skin    incision.” “EEG was continuously recorded via an Aspect B-500    monitor and BIS was calculated in real time from bilateral    frontocentral channels displayed on the monitor.” “Therefore, the    adjunctive use of opioid analgesics confounds the use of BIS as a    measure of anesthetic adequacy when movement response to skin    incision is used as the primary end point.”-   18.-   Muthuswamy J, Sharma A, described in J Clin Monit September 1996,    PMID: 8934342, UI: 97088404 “A study of electroencephalographic    descriptors and end-tidal concentration in estimating depth of    anesthesia.” In this study, Muthuswamy and Sharma describe:    “OBJECTIVE: To study the usefulness of three    electro-encephalographic descriptors, the average median frequency,    the average 90% spectral edge frequency, and a bispectral variable    were used with the anesthetic concentrations in estimating the depth    of anesthesia. METHODS: Four channels of raw EEG data were collected    from seven mongrel dogs in nine separate experiments under different    levels of halothane anesthesia and nitrous oxide in oxygen.”    “CONCLUSIONS: The bispectral variable seems to reduce the    non-linearity in the boundary separating the class of non-responders    from the class of responders. Consequently, the neural network based    on the bispectral variable is less complex than the neural network    that uses a power spectral variable as one of its inputs.”-   19.-   Shils J L, Litt M, Skolnick B E, Stecker M M, described in    Electroencephalogr Clin Neurophysiol February 1996, PMID: 8598171,    UI: 96173435 “Bispectral analysis of visual interactions in humans.”    In this study, Shils & colleagues describe: “We used non-linear    spectral analysis, in particular the bispectrum, to study    interactions between the electrocerebral activity resulting from    stimulation of the left and right visual fields. The stimulus    consisted of two squares, one in each visual field, flickering at    different frequencies. Bispectra, bichoherence and biphase were    calculated for 8 subjects monocularly observing a visual stimulus.”    “These results illustrate how bispectral analysis can be a powerful    tool in analyzing the connectivity of neural networks in complex    systems. It allows different neuronal systems to be labeled with    stimuli at specific frequencies, whose connections can be traced    using frequency analysis of the scalp EEG.”-   20.-   Leslie K, Sessler D I, Schroeder M, Walters K, described in Anesth    Analg December 1995, PMID: 7486115, UI: 96079788 “Propofol blood    concentration and the Bispectral Index predict suppression of    learning during propofol/epidural anesthesia in volunteers.” In this    study, Leslie & colleagues describe: “Propofol is often used for    sedation during regional anesthesia. We tested the hypothesis that    propofol blood concentration, the Bispectral Index and the 95%    spectral edge frequency predict suppression of learning during    propofol/epidural anesthesia in volunteers. In addition, we tested    the hypothesis that the Bispectral Index is linearly related to    propofol blood concentration.” “The Bispectral Index value when    learning was suppressed by 50% was 91+/−1. In contrast, the 95%    spectral edge frequency did not correlate well with learning. The    Bispectral Index decreased linearly as propofol blood concentration    increased (Bispectral Index=−7.4.[propofol]+90; r2=0.47, n=278).    There was no significant correlation between the 95% spectral edge    frequency and propofol concentration. In order to suppress learning,    propofol blood concentrations reported to produce amnesia may be    targeted. Alternatively, the Bispectral Index may be used to predict    anesthetic effect during propofol sedation.”-   21.-   Sebel P S, Bowles S M, Saini V, Chamoun N, described in J Clin Monit    March 1995, PMID: 7760092, UI: 95280046 “EEG bispectrum predicts    movement during thiopental/isoflurane anesthesia.” In this study,    Sebel & colleagues describe: “OBJECTIVE. The objective of our study    was to test the efficacy of the bispectral index (BIS) compared with    spectral edge frequency (SEF), relative delta power, median    frequency, and a combined univariate power spectral derivative in    predicting movement to incision during isoflurane/oxygen    anesthesia.” “CONCLUSIONS. When bispectral analysis of the EEG was    used to develop a retrospectively determined index, there was an    association of the index with movement. Thus, it may be a useful    predictor of whether patients will move in response to skin incision    during anesthesia with isoflurane/oxygen.”-   22.-   Kearse L A Jr, Manberg P, Chamoun N, deBros F, Zaslaysky A,    described in Anesthesiology December 1994, PMID: 7992904, UI:    95085072 “Bispectral analysis of the electroencephalogram correlates    with patient movement to skin incision during propofol/nitrous oxide    anesthesia.” In this study, Kearse & colleagues describe:    “BACKGROUND: Bispectral analysis is a signal-processing technique    that determines the harmonic and phase relations among the various    frequencies in the electroencephalogram. Our purpose was to compare    the accuracy of a bispectral descriptor, the bispectral index, with    that of three power spectral variables (95% spectral edge, median    frequency, and relative delta power) in predicting patient movement    in response to skin incision during propofol-nitrous oxide    anesthesia.” “CONCLUSIONS: The bispectral index of the    electroencephalogram is a more accurate predictor of patient    movement in response to skin incision during propofol-nitrous oxide    anesthesia than are standard power spectrum parameters or plasma    propofol concentrations.”-   23.-   Sigl J C, Chamoun N G, described in J Clin Monit November 1994,    PMID: 7836975, UI: 95138762 “An introduction to bispectral analysis    for the electroencephalogram.” In this study, Sigl and Chamoun    describe: “The goal of much effort in recent years has been to    provide a simplified interpretation of the electroencephalogram    (EEG) for a variety of applications, including the diagnosis of    neurological disorders and the intraoperative monitoring of    anesthetic efficacy and cerebral ischemia. Although processed EEG    variables have enjoyed limited success for specific applications,    few acceptable standards have emerged. In part, this may be    attributed to the fact that commonly used signal processing tools do    not quantify all of the information available in the EEG. Power    spectral analysis, for example, quantifies only power distribution    as a function of frequency, ignoring phase information. It also    makes the assumption that the signal arises from a linear process,    thereby ignoring potential interaction between components of the    signal that are manifested as phase coupling, a common phenomenon in    signals generated from nonlinear sources such as the central nervous    system (CNS).”-   24.-   Kearse L A Jr, Manberg P, DeBros F, Chamoun N, Sinai V, described in    Electroencephalogr Clin Neurophysiol March 1994, PMID: 7511501, UI:    94192475 “Bispectral analysis of the electroencephalogram during    induction of anesthesia may predict hemodynamic responses to    laryngoscopy and intubation.” In this study, Kearse and colleagues    describe: “The use of electroencephalography as a measure of    adequacy of anesthesia has achieved limited success. Our purpose was    to determine whether the non-linear properties of the    electroencephalogram (EEG) as defined by the bispectral index was a    better predictor of autonomic responses to endotracheal intubation    during opioid-based anesthesia than the linear statistical    properties of the EEG formulated by power spectral analysis.” “There    was a significant difference between response groups as measured by    the bispectral index which distinguished responders from    non-responders independently of the amount of drug given. None of    the variables of power spectral analysis accurately distinguished    responder from non-responder.”-   The HCM System is designed to use conventional low cost electrodes    in conjunction with wireless interface device to reduce the hazards    and difficulties associated with wiring patients during operational    procedures. Furthermore the HCM System utilises a unique method of    displaying the charge status of the wireless electrode module by way    of simple led display representing the available charge time, where    each hour (or 2 hours) of charged usage time available is    represented by a LED display. The HCM System wireless device also    provides a simple fool-proof means of recharging the wireless module    by utilising a unique proximity RF charging technique.    The following papers present some of the difficulties of state of    the art which are overcome by the HCM System:-   25.-   Yli-Hankala A, Vakkuri A, Annila P, Korttila K, described in Ada    Anaesthesiol Scand May 1999, PMID: 10342003, UI: 99273549 “EEG    bispectral index monitoring in sevoflurane or propofol anaesthesia:    analysis of direct costs and immediate recovery.” In this study,    Yli-Hankala and colleagues describe: “BIS monitoring decreased the    consumption of both propofol and sevoflurane and hastened the    immediate recovery after propofol anaesthesia. Detailed cost    analysis showed that the monitoring increased direct costs of    anaesthesia treatment in these patients, mainly due to the price of    special EEG electrodes used for relatively short anaesthesias.”-   26-   EEG power spectrum during repeated transitions from consciousness to    unconsciousness. R. J Gajrai, M. Doi, H. Mantzzaridis and G. N. C.    Kenny. British Journal of Anaesthesia 1998.-   29-   Moira L. Steyne-Ross and D. A. Steyne-Ross, of Department of    Anaesthetics, Waikato Hospital, Hamilton, New Zealand describe    “Theoretical electroencephalogram stationary spectrum for    white-noise-driven cortex:Evidence for a general anaesthetic-induced    phase transition” This paper describes an increase in EEG spectral    power in the vicinity of the critical point of transition into    comatose-unconsciousness.-   The HCM System applies the capability to predict the amplitude of an    EEG signal during administration of an anaesthetic drug as one of    the weighted inputs for an improved in depth anaesthesia monitoring    system.-   30.-   Analysis of the EEG Bispectrum, auditory evoked potentials and the    EEG power spectrum during repeated transitions from consciousness to    unconsciousness. R. J. Gajraj, M. Doi, H. Mantzaridis and G. N. C.    Kenny. British Journal of Anaesthesia 1998.-   31.-   Differentiating Obstructive and Central Sleep Apnea Respiratory    Events through Pulse Transit Time. Jerome Argod, Jean-Louis Pepin,    and Patrick Levy. Resp Crit Care Med 1998 Vol 158 pp 1778-1783.-   32.-   Pulse Transit Time: an appraisal of potential clinical applications.    Robin P Smithj, Jerome Argod, Jean-Louis Pepin, Patrick A Levy.    Thorax 1999; 54:452-458.-   33.-   An Introduction to Bispectral Analysis for the electroencephelogram.    Jeffrey C. Sigl. PhD, and Nassib G. Chamoun, M S. 1994 Little, Brown    and Company.-   34.-   Allan Rechtschaffen and Anthony Kales, Editors of A Manual of    Standardized Terminology, Techniques and Scoring System for Sleep    Stages of Human Subjects, Brain Information Service/Brain Research    Institute, University of California, Los Angeles, Calif. 90024.-   35.-   EEG Arousals: Scoring Rules and Examples. A Preliminary Report from    the Sleep Disorders Atlas Task Force of the American Sleep Disorders    Association. Sleep, Vol. 15 No. 2, 1992.-   36.-   95% spectral edge analysis is the point on the spectral power curve    of a sample of data, which is measured at the 95% point on the    frequency axis, where the Y axis represents the frequency band    power.-   For example refer to FIG. 49 which shows a power spectral curve of    sample data.-   37.-   The Biomedical Engineering Handbook. Joseph D. Bronzino. 1995 pages    840 to 852. Signal averaging.-   38. The Fourier Transform in Biomedical Engineering. Introduction to    the Fourier Transform. T. M. Peters. 1998. Chapter 1.-   39.-   Biomedical Instruments Theory and Design. Second Edition. Walter    Welkowitzl 992. The frequency spectrum. Pages 10 to 19.-   40.-   The Biomedical Engineering Handbook. Joseph D. Bronzino. 1995-   Bioloectric Phenomena. Craig S. Henriquez. Chapter 11.-   41-   The Biomedical Engineering Handbook. Joseph D. Bronzino. 1995-   Biomedical Signals: Origin and Dynamic Characteristics;    Frequency-Domain Analysis. Chapter 54.-   42.-   The Biomedical Engineering Handbook. Joseph D. Bronzino. 1995-   Anaesthesia Delivery Systems. Chapter 86.-   43.-   The Biomedical Engineering Handbook. Joseph D. Bronzino. 1995-   Measurement of Sensory-Motor Control Performance Capacities. Chapter    145.-   44.-   Principles and Practice of Sleep Medicine, Second edition 1994,    Kryger Roth Dement. Chapter 89 Monitoring and Staging Human Sleep by    Mary Carskadon and Allan Rechtschaffen.-   45.-   Patent Reference: AU 632432 Analysis System for Physiological    Variables. Burton and Johns, 1989.-   46.-   An improved method for EEG analysis and computer aided sleep    scoring. (½ period amplitude abstract—Johns and Burton 1989.-   Abstracts of Conference Manual.-   47.-   Atlas of Adult Electroencephalography. Warren T Blume, Masaka    Kaibara, Raven Press, 1995. Artifacts, Chapter 2.-   48.-   The Biomedical Engineering Handbook. Joseph D. Bronzino. 1995    Higher-Order Spectra in Biomedical Signal Processing. Pages 915-916.-   49.-   Barrett-Dean Michelle, Preliminary Investigation of the Compumedics    Mattress sensor in a clinical setting. St Frances Xavier Cabrini    Hospital, Malvern, Victoria.-   ASTA, 1999.-   50.-   Modified R&K utilising frequency compensation techniques to best    approximate non-conventional R&K EEG electrode position    recommendations (34).-   51.-   Bispectral Analysis of the Rat EEG During Various Vigilance States.    Taiking Ning and Joseph D. Bronzino. IEEE transactions on biomedical    engineering. April 1989.-   52.-   Trademarks associated with Aspect monitoring;-   BIS®-   Bispectral Index®-   A2000™-   53.-   Effects of paced respiration and expectations on physiological and    physiological responses to threat.-   McCaul K D, Solomon S, Holmes D S, Journal of Personality and Social    Psychology 1979, Vol 37, No 4, 564-571-   54.-   Casino gambling increases heart rate and salivary cortisol in    regular gamblers. Meyer G, Hauqffa B P, Schedlowski M, Pawlak C,    Stadler M A, Exton M S Biol Psychiartry 2000 Nov. 1; 48(9):948-53-   ABSTRACT-   55.-   Heart rate variability, trait anxiety, and perceived stress among    physically fit men and women.-   Dishman R K, Nakamura Y, Garcia M E, Thompson R W, Dunn A L, Blair    SN Int J Psychophysiol 2000 August; 37(2):121-33-   56.-   Effects of short-term psychological stress on the time and frequency    domains of heart variability-   Delaney J P, Brodie D A-   Percept Mot Skills 2000 October; 9(2):515-24-   ABSTRACT-   57.-   Heart rate variability in depressive and anxiety disorders-   Gorman J M, Sloan RP-   Am Heart J 2000 October; 140(4 Suppl):77-83-   ABSTRACT-   58.-   Chronic stress effects blood pressure and speed of short-term    memory.-   Brand N, Hanson E, Godaert G-   Percept Mot Skills 2000 August; 91(1):291-8-   ABSTRACT-   59.-   Effects of Paced Respiration and Expectations on Physiological and    Psychological Responses to Threat.-   Kevin D. McCaul, Sheldon Solomon, and David S. Holmes-   University of Kansas, Journal of Personality and Social Psychology-   1979, Vol. 37, No 4, 564-571-   PAPER-   60.-   Heart. Rate Variability, trait anxiety, and perceived stress among    physically fit men and women-   Rod K. Dishman, Yoshia Nakamura, Melissa E. Garcia, Ray W. Thompson,    Andrea L. Dunn, Steven N. Blair-   16 Nov. 1999, International Journal of Psychophysiology 37 (2000)    121-133-   61-   Auditory evoked potential index: a quantitative measure of changes    in auditory evoked potentials during general anaesthesia-   H. Mantzaridis and G. N. C. Kenny-   Anaesthesia, 1997, 52 pages 1030-1036-   62-   Concept for an intelligent anaesthesia EEG monitor-   W. NAHM, G. STOCKMANNS, J. PETERSEN, H. GEHRING, E. KONECNY, H. D.    KOCHS and E. KOCHS-   Med. Inform. (1999), vol. 24. NO. 1-9-   63-   Monitoring depth of anaesthesia-   G. Schneider and P. S. Sebel-   European Journal of Anaesthesiology 1997, 14 (Suppl. 15), 21-28-   64-   Clinical usefulness of the bispectral index for titrating propofol    target effect-site concentration-   M. Struys, L. Versichelen, G. Byttebier, E. Mortier, A. Moerman    and G. Roily Anaesthesia, 1998, 53, pages 4-12-   65-   Genetic dependence of the electroencephalogram bisprectrum-   JOEL. WHITTON, SUSAN M. ELGIE, HERB KUGEL, AND HARVY MOLDOFSKY-   Electroencephalography and clinical Neurophysiology, 1985, 60;    293-298-   66-   Assessment of power spectral edge for monitoring depth of    anaesthesia using low methohexitone infusion-   Peter S. Withington, John Morton, Richard Arnold, Peter. S. Sebel    and Richard Moberg-   International Journal of Clinical Monitoring and Computing 3:    117-122, 1986-   67-   Analysis of the interrelations between frequency band of the EEG by    means of the bispectrum.-   Electroencephalography and clinical neurophysiology. International    Federation-7^(th) Congress. Free communications in EEG.-   Kleiner B; Huber P J, Dumermuth G;-   68-   GSR or Skin Response-   Biomedical Instruments, Inc.-   WWW.bio-medical.com/Gsr.html 26/0201-   69-   Awareness during general anaesthesia: is it worth worrying about ?-   MJA Vol 174 5 March 2001-   70-   Women take longer to recover from operations and are more likely to    suffer side-effects than men during surgery.-   British Medical Journal, Mar. 23, 2001.-   (reference—Age Mar. 24, 2001)-   71-   Compumedics Siesta patient monitoring system.-   72-   Compumedics E-Series patent monitoring system.-   73-   Compumedics Profusion Software patient monitoring system.-   74-   Recall of intraoperative events after general anaesthesia and    cardiopulmonary bypass; Phillips A A, McLean R F, Devitt J H,    Harrington E M; Canadian Journal of Anaesthesia; 1993 Oct.-   75-   Patient satisfaction after anaesthesia and surgery: results of a    prospective survey of 10,811 patients; Myles P S, Williams D L,    Hendrata M, Anderson H, Weeks A M; British Journal of Anaesthesia;    2000 Jan.-   76-   EEGs, EEG processing, and the bispectral index; Todd M M;    Anesthesiology; 1998 Oct.-   77-   Detecting awareness during general anaesthetic caesarean section. An    evaluation of two methods; Bogod D G, Orton J K, Yau H M, Oh T E;    Anaesthesia; 1990 Apr.-   78-   Oesophageal contractility during total i.v. anaesthesia with and    without glycopyrronium; Raftery S, Enever G, Prys-Roberts C; British    Journal of Anaesthesia; 1991 May.-   79-   Effect of surgical stimulation on the auditory evoked response;    Thornton C, Konieczko K, Jones J G, Jordan C, Dore C J, Heneghan C P    H; British Journal of Anaesthesia; 1988 Mar.-   80-   Comparison of bispectral index, 95% spectral edge frequency and    approximate entropy of the EEG, with changes in heart rate    variability during induction of general anaesthesia; Sleigh J W,    Donovan J; British Journal of Anaesthesia; 1999 May.-   81-   Bispectral index monitoring allows faster emergence and improved    recovery from propofol, alfentanil, and nitrous oxide anesthesia.    BIS Utility Study Group; Gan T J, Glas P S, Windsor A, Payne F,    Rosow C, Sebel P, Manberg P; Anesthesiology; 1997 Oct.-   82-   Why we need large randomized studies in anaesthesia; Myles P S;    British Journal of Anaesthesia; 1999 Dec.-   83-   U.S. Pat. No. 5,381,804, Aspect Medical Systems, Inc Jan. 17, 1995    describes a monitor for receiving electrical signals from a living    body.-   84-   U.S. Pat. No. 5,458,117, Aspect Medical Systems, Inc October. 17    1995 describes a cerebral biopotential analysis system and method.-   85-   U.S. Pat. No. 5,320,109 Aspect Medical Systems, Inc Jun. 14, 1994    describes a celebral biopotential analysis system and method.-   86-   It has been reported that Wrist actigraphic recordings may    differentiate sleep and wakefulness with a 94.5% agreement with    standard polysomnography (Mullaney et al. 1980).-   Other Wrist actigraphic studies studies have reported a 91.8%    agreement in healthy subjects, 85.7% in patients with obstructive    sleep apnea syndrome, 78.2% in patients with insomnia, and 89.9% in    children Sadeh et al. (1989) (21).-   87.-   Medical Dictionary. 1997 Merriam-Webster, Incorporated.    http://www.intelihealth.com/IH/-   88.-   The American Heritage® Dictionary of the English Language-   http://www.bartleby.com/61/-   89.-   William Thomas Gordon Morton first demonstrated what is today    referred to as surgical anaesthesia (89).-   Pubmed search-   90-   In Australia about 1 million people a year undergo general    anaesthesia. Of these 1 million people about 5 people die each year,    as a direct result of the anaesthesia, while about 3000 more will be    inadequately anaesthetised. These people inadequately anaesthetised    will experience a range of symptoms from hearing recall while    undergoing a medical procedure, sight recall from premature recovery    and the early opening of eyes, stress and anxiety from experiencing    paralysis while some degree of mental awareness to the medical    procedure being instigated, memory recall from having some degree of    consciousness, operation mishaps can occur in cases where the    subject's state of paralysis is not adequate and leads to movement    of the subject's body during incision.-   91-   In fact, even hospitals such as Melbourne's Alfred Hospital, which    demonstrated one of the world's lowest reported incidences of    consciousness under general anaesthesia, still have an incidence    rate of 1 in 1000 patients (for consciousness under anaesthesia)    (91).-   Pubmed search-   92-   Up-to-date there has been no way to determine whether a patient is    asleep during a medical procedure (according to University of    Sydney-Australia's Web site, introductory paper on anaesthesia).-   Pubmed search-   93-   Furthermore, the discovery in 1942 Canadian anaesthetists determined    (Sir Walter Raleigh had known in 1596 that the indigenous people of    Bolivia had been using an American plant derivative called curare to    cause paralysis) that neuromuscular blocking drugs could be    developed (93). Since 1942 these drugs have revolutionised surgery,    particularly abdominal and chest operations where muscle contraction    had made cutting and stitching almost impossible.-   Pubmed search-   94-   Anaesthetists tend to overestimate the amount of anaesthetic drug    usage by up to 30%. This overestimation has consequences in relation    to a patient's health, recovery time and financial costs to health    services.-   (Age Article—Eyes Wide Shut)-   Pubmed search-   95-   The challenge to monitor for appropriate or optimum anaesthesia is    even further demonstrated with classic experiments such as that of    psychiatrist Bernard Levin in 1965, when 10 patient's who were read    statements during anaesthesia, later had no recall of the statements    when questioned after surgery. However, of the same patient's under    hypnosis four could quote the words verbatim and another four could    remember segments, but became agitated and upset during questioning.-   Pubmed search-   96-   An adequately anaesthetised patient should not “feel”, “smell”,    “see” or “taste” anything until they regain consciousness.-   (Age Article—Eyes Wide Shut)-   Pubmed search-   97-   In 1998 Dr David Adams of New York's Mount Sinai Medical Centre    replayed audiotapes of paired words (boy/girl, bitter/sweet,    ocean/water . . . ) to 25 unconscious heart surgery patients.    Approximately four days after the operation, the patients listened    to a list of single words. Some of these words had been played while    they were unconscious during their former operation. The patients    were asked to respond to each word with the first word that came    into their minds. The patient was found to be significantly better    at free-associating the word pairs they had already encountered than    those they had not. It was apparent that the patients had heard the    information and remembered it.-   (Age Article—Eyes Wide Shut)-   Pubmed search-   98-   It appears that while a smaller number of patient's have conscious    memories of their experiences on the operating table, a larger    number have unconscious recollections. While positive messages    during surgery may have desired consequences others can have    undesirable results.-   Pubmed search-   99-   The PERCLOS Monitor-   Reference: http://www.cmu.edu.cmri/drc/drcperclosfr.html, Dec. 10,    2000-   100.-   Drowsy Driver Detection System-   http://www.jhuapl.edu/ott/newtech/soft/DDDSystem/benefits.htm-   101.-   Driver Drowsiness Literature Review & Perspective; Cause, effects,    detection, PSG methods, Bio-behavioural, Physiological, safety    air-bags business case, practicality, ease-of-use, ethical    implications & alarms.-   Burton, June 2001.-   102.-   Proprietary, Zilberg Eugene, Ming Xu May 2001. Compumedics    preliminary Vigilance Project Report on Drowsiness and movement    sensor (seat & steering wheel)—correlation analysis (May, 2001).-   103.-   Burton David, Methods and Apparatus for Monitoring Human    Consciousness, U.S. Provisional Patent Application 60/298,011 filed    13 Jun. 2001.-   104.-   Iani C, Gopher D, Lavie P-   Effects of Task Difficulty and Invested Mental Effort of Peripheral    Vascoconstriction. SLEEP, Vol-24, Abstract Supplement 2001.-   105-   Autonomic Activation Index (AAI)—A New Marker of Sleep Disruption.    Pillar G, Shlitner A, Lavie P. SLEEP, Vol-24, Abstract Supplement    2001.-   106.-   Lac, Leon. PAT perspective—how well is pulse wave amplitude related    top PAT?. Correspondence with DB. Jun. 27, 2001.-   107.-   Peter G. Catcheside, R. Stan Orr, Siau Chien Chiong, Jeremy Mercer,    Nicholas A, Saunders. Peripheral cardiovascular responses provide    sensitive markers of acoustically induced arousals from NREM sleep.-   108.-   Michael H. Pollok and Paul A. Obrist. Aortic-Radial Pulse Transit    Time and ECG Q-Wave to Radial Pulse Wave Interval as Indices of    Beat-By-Beat Blood Pressure Change. Psychophysiology. Vol. 20 No.    11983.-   109.-   PAT signal Provides New Marker of Sleep Quality, Respiratory and    Cardiovascular Disorders. http://www.talkaboutsleep.com/news/PAT    signal.htm.-   Chicagao, Ill., Jun. 7, 2001-   110.-   Todd, Michael M. MD. EEGs, EEG Processing, and Bispectral Index.    Anaesthesiology. Vol. 89(4), pp 815-817. October 1998-   111-   A Primer for EEG Signal Processing in Anaesthesia.-   Anaesthesiology. Vol. 89(4), pp 980-1002. October 1998-   112. Lippincott-Raven, 1997. Evoked Potentials in Clinical Medicine.    Third Edition. a) Click Intensity. CH8, 179. b) Click Polarity. CH8,    PP183. c) Stimulus Delivery Apparatus. CH8, PP188.-   113. Nieuwenhuijs, D.; Coleman, E. L.; Douglas, N. J.; Drummond, G.    B.; Dohan, A. Bispectral index values and spectral edge frequency of    different stages of physiologic sleep. Anesth. Analg.-   114. Kryger, Roth, Dement. Principles and Practice of Sleep    Medicine. Second edition, 2000.

APPENDIX II Glossary

-   AMPLITUDE One half the peak-to-peak height of a sinusoid, usually    measured in volts or microvolts (μV). (33)-   Anesthesia or Anaesthesia Noun:-    1. Total or partial loss of sensation, especially tactile    sensibility, induced by disease, injury, acupuncture, or an    anesthetic, such as chloroform or nitrous oxide. 2. Local or general    insensibility to pain with or without the loss of consciousness,    induced by an anesthetic. 3. A drug, administered for medical or    surgical purposes, that induces partial or total loss of sensation    and may be topical, local, regional, or general, depending on the    method of administration and area of the body affected.-    Word History:-    The following passage, written on Nov. 21, 1846, by Oliver Wendell    Holmes, a physician-poet and the father of the Supreme Court justice    of the same name, allows us to pinpoint the entry of anesthesia and    anesthetic into English: “Every body wants to have a hand in a great    discovery. All I will do is to give you a hint or two as to names—or    the name—to be applied to the state produced and the agent. The    state should, I think, be called ‘Anaesthesia’ [from the Greek word    anaisthēsia, “lack of sensation”]. This signifies insensibility . .    . . The adjective will be ‘Anaesthetic.’ Thus we might say the state    of Anaesthesia, or the anaesthetic state.” This citation is taken    from a letter to William Thomas Green Morton, who in October of that    year had successfully demonstrated the use of ether at Massachusetts    General Hospital in Boston. Although anaesthesia is recorded in    Nathan Bailey's Universal Etymological English Dictionary in 1721,    it is clear that Holmes really was responsible for its entry into    the language. The Oxford English Dictionary has several citations    for anesthesia and anesthetic in 1847 and 1848, indicating that the    words gained rapid acceptance-   BICOHERENCE A normalised measure of phase coupling in a signal,    ranging from 0% to 100%. (33)-   BISPECTRAL INDEX A mutlivariate measure incorporating bispectral and    time-domain parameters derived from the EEG. (33)-   BISPECTRUM A measure of the level of phase coupling in a signal, as    well as the power in the signal. The bispectrum can be described as    a measure of the actual level of phase coupling that exists in the    EEG signal, with the phase angles of the components at their actual    values. (33)-   COMPONENT One of the sinusoids summed together in a Fourier series    to represent a signal. (33)-   Consciousness Function: noun-    1: the totality in psychology of sensations, perceptions, ideas,    attitudes, and feelings of which an individual or a group is aware    at any given time or within a given time span <altered states of    con•scious•ness, such as sleep, dreaming and hypnosis—Bob Gaines>-    2: waking life (as that to which one returns after sleep, trance,    or fever) in which one's normal mental powers are present <the ether    wore off and the patient regained con•scious•ness>-    3: the upper part of mental life of which the person is aware as    contrasted with unconscious processes (87)-    1. The state or condition of being conscious. 2. A sense of one's    personal or collective identity, including the attitudes, beliefs,    and sensitivities held by or considered characteristic of an    individual or group: Love of freedom runs deep in the national    consciousness. 3a. Special awareness or sensitivity: class    consciousness; race consciousness. b. Alertness to or concern for a    particular issue or situation: a movement aimed at raising the    general public's consciousness of social injustice. 4. In    psychoanalysis, the conscious. (88)-   EPOCH A series of successive, equal time segments (overlapping or    contiguous) into which the data series x(k) is divided. (33)-   FEATURES Descriptive parameters extracted from a signal and    correlated with some information of interest, such as a particular    cerebral state. (33)-   FOURIER SERIES A representation of a signal as a sum of sinusoid    components of different frequencies and amplitudes. (33)-   FOURIER TRANSFORM A mathematical process that converts a time signal    to its representation in terms of the amplitudes and frequencies of    its sinusoid components. (33)-   FREQUENCY The rate at which a signal or sinusoid oscillates, usually    measured in cycles per second (Hz). (33)-   FREQUENCY DOMAIN A representation of a signal in which amplitude or    power is a function of frequency. (33)-   FREQUENCY RESOLUTION The spacing in hertz between successive values    of the Fourier transform. (33)-   FUNDAMENTAL A component of an output signal that is not an IMP. (33)-   HERTZ (Hz) A measure of frequency; equivalent to cycles per second.    (33)-   REAL TRIPLE PRODUCT (RTP) A measure of the maximum possible degree    of phase coupling that would result if the phase angle of each and    every component of a signal were exactly identical. It is also a    function of signal power. The ratio of the bispectrum to the square    root of the real triple product, which expresses the normalized    degree of phase coupling in the EEG range (ranging from 0 to 100%)    is defined as the bicoherence.-   System Refers to the device or apparatus forming the basis of    invention. This system typically contains physiologically recording    capabilities for 1 or more channels of physiological data, display    viewing capabilities for viewing or reviewing one or more channels    of physiological data, data analysis and reporting capabilities, and    data recording and archiving and retrieval capabilities, for the    purpose of providing a device for the investigation of a patient's    state of consciousness or vigilance.-   HCM system Denotes Human Consciousness Monitoring system including    methods and apparatus for monitoring, sensing, tracking, analysing,    storing, logging and/or displaying, in the context of the present    invention, data related to the state of mind or state of    consciousness of human and other sentient subjects.-   System-generated audio Refers to the audio click, which can be    applied to the patient's ear or ears during an operating procedure,    for example.-   Unconscious Adjective:-    Lacking awareness and the capacity for sensory perception; not    conscious. 2. Temporarily lacking consciousness. 3. Occurring in the    absence of conscious awareness or thought: unconscious resentment;    unconscious fears. 4. Without conscious control; involuntary or    unintended: an unconscious mannerism.-    Noun:-    The division of the mind in psychoanalytic theory containing    elements of psychic makeup, such as memories or repressed desires,    that are not subject to conscious perception or control but that    often affect conscious thoughts and behavior.-    Other forms:-    un•con'scious•ly—ADVERB-    un•con'scious•ness—NOUN (88)-   Unconsciousness Function: adjective-    1: not marked by conscious thought, sensation, or feeling    <un•con•scious motivation>-    2: of or relating to the unconscious-    3: having lost consciousness <was un•con•scious for three days>-    un•con•scious•ly adverb-    un•con•scious•ness noun (87)-    Alert watchfulness (88)-   Vigilance Function: noun-    : the quality or state of being wakeful and alert: degree of    wakefulness or responsiveness to stimuli-    vig•i•lant/-l&nt/adjective (87)-   Unconsciousness Function: adjective-    1: not marked by conscious thought, sensation, or feeling    <un•con•scious motivation>-    2: of or relating to the unconscious-    3: having lost consciousness <was un-conscious for three days>-    un•con•sciously adverb-    un•con•scious•ness noun (87)-   Subject This word can be interchanged within context of this    document for “patient”.-   Patient This word can be interchanged within context of this    document for “subject”.-   Vagal modulation definition; The parasympathetics to the heart are    contained in the vagus nerves. The vagus nerves. Stimulation of    these nerves causes slowing of the heart while cutting the    parasympathetics causes the heart rate to increase. Vagal modulation    relates to the modulation of the vagus nerves (Stedmans, Medical    Dictionary, 2000), which in turn relates to the slowing of the heart    (Vander et al, Human Physiology, 1970 PP 241).-   Relationship of Bispectrum, Real Triple Product and Bicoherence The    bispectrum can be described as a measure of the actual level of    phase coupling that exists in the EEG signal, with the phase angles    of the components at their actual values.-    The real triple product is a measure of the maximum possible degree    of phase coupling, which could result if the phase angle of each and    every component of the EEG were exactly identical. The ratio of the    bispectrum to the square root of the real triple product, which    expresses the normalized degree of phase coupling in the EEG range    (ranging from 0 to 100%) is defined as the bicoherence.-   CONTEXT ANALYSIS Refers to whether the patient's is entering a state    of consciousness or emerging from unconsciousness. ½ period    amplitude analysis (ref 3, 4, 8, 9) is a method for determining the    stage od sleep a subject is in. Stages include WAKE, STAGE 1, STAGE    2, STAGE 3, STAGE 4 AND REM SLEEP.

ABBREVIATIONS

-   ADMS Anaesthesia Depth of Monitoring System.-   Bi Bispectral index.-   B Bicoherence derivattive of the EEG signal.-   SSA Sleep Staging Analysis.-   AEPi Audio Evoked Potential index.-   TUC Transition From Unconsciousness to Consciousness.-   TCU Transition From Consciousness to Unconsciousness-   CIAi Comprehensive Integrated Anaesthesia index. The main function    and output of the ADMS.-   DOA Depth Of Anaesthesia.-   CALPAT Calibrated Patient (values).-   CP Calibrated Patient.-   IDDZA Impirical Data Display Zone A.-   IDDZB Impirical Data Display Zone B.-   IDDZC Impirical Data Display Zone C.-   IDDZD Impirical Data Display Zone D.-   CPDZA Calibrated Patient DisplayZone A.-   CPDZB Calibrated Patient DisplayZone B.-   CPDZC Calibrated Patient DisplayZone C.-   CPDZD Calibrated Patient DisplayZone D.-   CPTUCBi Calibrated Patient data for Transition from Unconsciousness    to Consciousness for Bi.-   CPTUCAEPi Calibrated Patient data for C1260Transition from    Unconsciousness to Consciousness C1230 for AEPi.-   CPTUCSSA Calibrated Patient Transition from Unconsciousness to    Consciousness for SSA.-   FE Forehead Electrodes-   EOG Electrooculogram-    The study of electrophysiology eye movement-    surface electrode signals (which show rapid activity with WAKE and    REM sleep stages).-   EEG Electroencephelogram-    The study of electrophysiology surface electrode signals    (electrical muscle energy, which decreases with sleep state).-   EMG Electromyography-    The study of electrophysiology eye movement surface electrode    signals (which show rapid activity with WAKE and REM sleep stages).-   SPL Sound Pressure Level-   C Consciousness-   U Unconsciousness-   TSW Transition from Sleep to Wake-   S1W>S SSA Stage 1 Wake to Sleep-   Bme Body Movement Event-    Detection of Body Movement (BM) relates to a physical movement of    the body such as detected by a pressure or vibration sensitive    sensors.-   Bmi Body Movement index-   Ae Arousal event-    Arousal refers to physiological events as can be cause by the    Central Nervous System (CNS), and may not always constitute a body    movement detection.-   Ai Arousal index-   DZ Display Zone Display Zones (DZ) of display represents the zones    of the ADMS display where defined phases or states can be measured.-   DZCT The critical Display Zones Critical Threshold (DZCT) of display    represents the values which are desired to be displayed in such a    manner that the user has an expanded viewing range (on meter    display, for example) compared to less critical display zones. In    the present invention the ability exists to define these said    “critical display zones” and in particular the critical display    zones can change subject to both the context of a subjects current    and past states of conscious/wake or unconscious/sleep.-   CD Current Data-   CDAEPi Current Patient Data AEPi (Value)-   IDAEPi Impirical Data AEPi (Value)-   CDTCUAEPi Current Data for TCU of AEPi. Current Data refers to    latest analysed real time data value.-   CDTCUBi Current Data for TCU of Bi. Current Data refers to latest    analysed real time data value.-   ID Impirical Data-   IDAEPi Impirical Data value for AEPi-   IDBi Impirical Data value for Bi-   CDTCUSSA Current Data for TCU of SSA. Current Data refers to latest    analysed real time data value.-   CPTUCSSA Calibrated Patient for Transition from Unconsciousness to    Consciousness for Sleep Staging Analysis.-   CDTUCAEPi Current Data for TUC of AEPi. Current Data refers to    latest analysed real time data value.-   CPTSWAEPi Calibrated Patient for Transition Context State from Sleep    to Wake for AEPi.-   DZTF Display Zone Transition Formula-   Zone A Patient emerging from Consciousness to Unconsciousness.-   Zone B Patient in unconscious state.-   Zone C Patient in unconscious state.-   Zone D Patient Transition from Unconsciousness to Consciousness.-   CA1W>S Context Analysis Change from WAKE to (sleep-stage 1, 2, 3, 4    or REM) ref 3, 4, 8, 9-   CA2W>S Context Analysis Change from sleep-stage 1 to (2 OR 3 OR 4 OR    REM) ref 3, 4, 8, 9-   CA3W>S Context Analysis Change from sleep-stage 2 to (3 OR 4 OR REM)    ref 3, 4, 8, 9-   CA4W>S Context Analysis Change from sleep-stage 3 to (4 OR REM) ref    3, 4, 8, 9-   CA5W>S Context Analysis Change from sleep-stage 4 to REM) ref 3, 4,    8, 9-   CA6S>W Context Analysis Change from sleep-stage REM to (WAKE OR 1,    2, 3 or 4) ref 3, 4, 8, 9-   CA7S>W Context AnalysisChange from sleep-stage 4 to (WAKE or 1 OR 2    OR 3) ref 3, 4, 8, 9-   CA8S>W Context Analysis Change from sleep-stage 3 to (WAKE OR 1    OR 2) ref 3, 4, 8, 9-   CA9S>W Context Analysis Change from sleep-stage 2 to (WAKE OR 1) ref    3, 4, 8, 9-   CA10S>W Context Analysis Change from sleep-stage 1 to WAKE ref 3, 4,    8, 9-   W Wake State-   STG1 Stage 1 of Sleep-   STG2 Stage 2 of Sleep-   STG3 Stage 3 of Sleep-   STG4 Stage 4 of Sleep-   REM REM Stage of Sleep % Represents start of comments, as applicable    to psuedo coding or lines of program code.-   IDOA Impirical Data Offset applied for zone A-   IDOB empirical Data Offset applied for zone B-   IDOC Impirical Data Offset applied for zone C-   IDOD Impirical Data Offset applied for zone D-   IDC Impirical Data Consciousness.-   IDU Impirical Data Unconsciousness.-   BM-Mz Body Movement Multi-zone sensor-   AEPiTF Audio Evoked Potential Transition Formula-   BiTF Bicoherence index Transition Formula-   SSATF Sleep Staging Analysis Transition Formula.-   EESM Electronics Electrode and Sensor Module

1. An apparatus for acquiring physiological data from a living being fordetermining the state of consciousness of said living being, comprising:means for acquiring at least one continuous biosignal; means forstimulating at least one evoked potential signal in said being; meansfor acquiring said at least one evoked potential biosignal; means forcalculating at least two indices from each acquired biosignal andselecting an index from the calculated indices to represent the state ofconsciousness from the living being wherein a first of said indices isderived from a transformation of raw signal data according to a firsttransformation method and the second and subsequent of said indices isderived from a transformation of raw signal data according to adifferent transformation method or methods from said firsttransformation method; wherein said first means for acquiring said firstbiosignal and said means for acquiring at least one evoked signalincludes a single means.
 2. The apparatus according to claim 1, whereinthe at least one biosignal is any one of, or a combination of, at leastone EEG signal or at least one muscular activation signal.
 3. Theapparatus according to claim 2, wherein the at least one muscularactivation signal is a measure of eyelid movement.
 4. The apparatusaccording to claim 2, wherein the EEG signal is a continuous signal. 5.The apparatus according to claim 2, including means for deriving saidevoked potential signal from the EEG signal.
 6. The apparatus accordingto claim 1, wherein the means for a acquiring the at least onecontinuous biosignal or means for acquiring the at least one evokedpotential signal includes at least one electrode sensor.
 7. Theapparatus according to claim 1, including means for monitoring the atleast one sensor for signal integrity.
 8. The apparatus according toclaim 1, including means for monitoring the at least one sensor forsignal quality.
 9. The apparatus according to claim 1, wherein the meansfor acquiring the at least one continuous biosignal or means foracquiring the at least one evoked potential signal includes at least onedisposable or semi-disposable sensor.
 10. The apparatus according toclaim 9, wherein said at least one disposable or semi-disposable sensorincludes means for activating an electrical energy source.
 11. Theapparatus according to claim 10, wherein the means for activating saidenergy source includes the packaging of said electrical energy source.12. The apparatus according to claim 1, wherein the means to acquire theat least one biosignal is an electrode sensor activatable in response topressure from an operator or user of said apparatus.
 13. The apparatusaccording to claim 1, wherein the means for stimulating the evokedpotential signal stimulates any one or a combination of somatosensory,auditory, or visual evoked response.
 14. The apparatus according toclaim 13, wherein the means for stimulating said auditory evokedresponse signal is a cochlear microphone.
 15. The apparatus according toclaim 13, whereby said auditory means induces a steady state responsesignal or any combination of signals inducing associated auditory evokedresponse or responses classified as the following, either singly or incombination greater than: 60 Hz ASSR, 40 Hz ASSR, or less than 20 HzASSR.
 16. The apparatus according to claim 1, including means fordisplaying the functional or operational status of any sensor.
 17. Theapparatus according to claim 1, wherein the means for inducing anauditory evoked potential response signal includes means for producingany one of or a combination of evoked response paradigms including: atleast one type of click stimulus according to an individual patient; atleast one response at spaced intervals within the at least one type ofclick stimulus; sounds with characteristics corresponding to white noiseor speech; oddball sound characteristics; unusual sound characteristics;masked noise sounds; unanticipated noise sounds; composite sounds;familiar sounds; and recognisable sounds in reference to said patient;wherein a combination of any sound stimulus is generated according to apredetermined sequence.
 18. The apparatus according to claim 17, whereinthe predetermined sequence is determined by determination meansincorporated within the apparatus.
 19. The apparatus according to claim1, including means for alerting an operator or user of the status of atleast one sensor.